Slope calculations
Lorenzo Ricolfi
Here, we calculate the Trophic magnification factor (TMF) for all those studies that do not report such value and/or its standard error. The TMF is calculated using PFAS concentrations and trophic levels (or stable nitrogen isotope values) data extracted from the included studies.
Data wrangling
# Uploading data
raw_data <- read.csv(here("metaDigitise/Raw_data.csv"))
head(raw_data)## Study_ID filename variable group_id mean
## 1 S_052 Cara_2022_1.png PFOA PFOA, species1, location 5 1.2131816
## 2 S_052 Cara_2022_1.png PFOA PFOA, species2, location 5 1.2988228
## 3 S_052 Cara_2022_1.png PFOA PFOA, species3, location 5 1.0097836
## 4 S_052 Cara_2022_1.png PFOA PFOA, species4, location 5 1.0418991
## 5 S_052 Cara_2022_1.png PFOA PFOA, species5, location 5 0.9776681
## 6 S_052 Cara_2022_1.png PFOA PFOA, species1, location 8 1.5718042
## sd n r se error_type plot_type software note
## 1 0.48708456 NA NA NA sd mean_error metaDigitise <NA>
## 2 0.13381444 NA NA NA sd mean_error metaDigitise <NA>
## 3 0.04282062 NA NA NA sd mean_error metaDigitise <NA>
## 4 0.15522475 NA NA NA sd mean_error metaDigitise <NA>
## 5 0.07493609 NA NA NA sd mean_error metaDigitise <NA>
## 6 0.32115465 NA NA NA sd mean_error metaDigitise <NA># Reshaping Data with pivot_wider
wide_data <- raw_data %>%
pivot_wider(
names_from = variable,
values_from = c(mean, sd),
names_glue = "{variable}_{.value}"
)
head(wide_data)## # A tibble: 6 × 96
## Study_ID filename group_id n r se error_type plot_type software
## <chr> <chr> <chr> <lgl> <lgl> <dbl> <chr> <chr> <chr>
## 1 S_052 Cara_2022_1… PFOA, s… NA NA NA sd mean_err… metaDig…
## 2 S_052 Cara_2022_1… PFOA, s… NA NA NA sd mean_err… metaDig…
## 3 S_052 Cara_2022_1… PFOA, s… NA NA NA sd mean_err… metaDig…
## 4 S_052 Cara_2022_1… PFOA, s… NA NA NA sd mean_err… metaDig…
## 5 S_052 Cara_2022_1… PFOA, s… NA NA NA sd mean_err… metaDig…
## 6 S_052 Cara_2022_1… PFOA, s… NA NA NA sd mean_err… metaDig…
## # ℹ 87 more variables: note <chr>, PFOA_mean <dbl>, TL_mean <dbl>,
## # PFUnDA_mean <dbl>, PFTrDA_mean <dbl>, ln_PFOS_mean <dbl>,
## # ln_PFOA_mean <dbl>, ln_PFNA_mean <dbl>, ln_PFDA_mean <dbl>,
## # ln_PFUnDA_mean <dbl>, ln_PFDoDA_mean <dbl>, ln_PFTrDA_mean <dbl>,
## # ln_PFTeDA_mean <dbl>, ln_FOSA_mean <dbl>, PFOS_mean <dbl>,
## # delta_15N_mean <dbl>, PFNA_mean <dbl>, PFDA_mean <dbl>, PFDoDA_mean <dbl>,
## # PFBA_mean <dbl>, log_PFHxS_mean <dbl>, log_PFOS_mean <dbl>, …S_052: Cara_2022
- 3 food webs: location 4, 5, and 8.
- [PFAS] regressed on the original scale. We log-transformed them before running linear models.
- Standard deviations were log-scaled using the Delta method (Taylor approximation).
- Data were extracted from plots (Figure 6) using metaDigitise.
Cara_2022 <- wide_data %>%
filter(Study_ID == "S_052") %>%
setDT() %>%
melt(id.vars = c("group_id"), na.rm = TRUE) %>%
dcast(group_id ~ variable, value.var = "value", fun.aggregate = max, na.rm = TRUE) %>%
mutate(group_id = gsub("^(\\w+\\s[^,]+)", "\\1,", group_id)) %>%
separate(group_id, into = c("PFAS", "species", "location"), sep = ",\\s*", extra = "drop")
# Divide the Cara_2022 dataset into locations (food webs).
Cara_2022_loc4 <- Cara_2022 %>%
filter(location == "location 4") %>%
mutate(across(ends_with("mean") | ends_with("sd"), ~ as.numeric(as.character(.)))) %>%
mutate(log_PFOA_mean = log10(PFOA_mean)) %>%
mutate(log_PFOA_sd = PFOA_sd/(PFOA_mean*log(10))) %>% #Delta method (Taylor aproximation)
select_if(~ !all(is.na(.)))
#write.csv(Cara_2022_loc4, file = here("Rdata", "Cara_2022_loc4.csv"))
Cara_2022_loc5 <- Cara_2022 %>%
filter(location == "location 5") %>%
mutate(across(ends_with("mean") | ends_with("sd"), ~ as.numeric(as.character(.)))) %>%
mutate(log_PFOA_mean = log10(PFOA_mean)) %>%
mutate(log_PFOA_sd = PFOA_sd/(PFOA_mean*log(10))) %>%
mutate(log_PFUnDA_mean = log10(PFUnDA_mean)) %>%
mutate(log_PFUnDA_sd = PFUnDA_sd/(PFUnDA_mean*log(10))) %>%
select_if(~ !all(is.na(.)))
#write.csv(Cara_2022_loc5, file = here("Rdata", "Cara_2022_loc5.csv"))
Cara_2022_loc8 <- Cara_2022 %>%
filter(location == "location 8") %>%
mutate(across(ends_with("mean") | ends_with("sd"), ~ as.numeric(as.character(.)))) %>%
mutate(log_PFOA_mean = log10(PFOA_mean)) %>%
mutate(log_PFOA_sd = PFOA_sd/(PFOA_mean*log(10))) %>%
mutate(log_PFTrDA_mean = log10(PFTrDA_mean)) %>%
mutate(log_PFTrDA_sd = PFTrDA_sd/(PFTrDA_mean*log(10))) %>%
select_if(~ !all(is.na(.)))
#write.csv(Cara_2022_loc8, file = here("Rdata", "Cara_2022_loc8.csv"))S_013: Martin_2004
- 3 food webs: benthic, pelagic, and whole food web.
- Benthic food web only has 2 trophic levels included in the regression. Standard error cannot be calculated.
- [PFAS] already ln-tranformed.
- Data were extracted from plots (Figure 3) using metaDigitise.
Martin_2004 <- wide_data %>%
filter(Study_ID == "S_013") %>%
select_if(~ !all(is.na(.))) %>%
setDT() %>%
melt(id.vars = c("group_id"), na.rm = TRUE) %>%
dcast(group_id ~ variable, value.var = "value", fun.aggregate = max, na.rm = TRUE) %>%
mutate(group_id = gsub("^(\\w+\\s[^,]+)", "\\1,", group_id)) %>%
separate(group_id, into = c("PFAS", "species", "food_web"), sep = ",\\s*", extra = "drop")
Martin_2004_pelagic <- Martin_2004 %>%
filter(food_web == "pelagic food web") %>%
mutate(across(ends_with("mean") | ends_with("sd"), ~ as.numeric(as.character(.))))
#write.csv(Martin_2004_pelagic, file = here("Rdata", "Martin_2004_pelagic.csv"))
Martin_2004_benthic <- Martin_2004 %>%
filter(food_web == "benthic food web") %>%
mutate(across(ends_with("mean") | ends_with("sd"), ~ as.numeric(as.character(.))))
#write.csv(Martin_2004_benthic, file = here("Rdata", "Martin_2004_benthic.csv"))
Martin_2004_whole <- Martin_2004 %>%
filter(food_web == "whole food web") %>%
mutate(across(ends_with("mean") | ends_with("sd"), ~ as.numeric(as.character(.))))
#write.csv(Martin_2004_whole, file = here("Rdata", "Martin_2004_whole.csv"))S_053: Koch_2020
- 2 food webs: aquatic and riparian
- [PFAS] regressed on the original scale, although plots have y-axis on the log scale. We log-transformed them before running linear models.
- Data were extracted from plots (Figure 4) using Webplot Digitizer.
- [PFAS] regressed against delta15N.
Koch_2020 <- wide_data %>%
filter(Study_ID == "S_053") %>%
select_if(~ !all(is.na(.))) %>%
setDT() %>%
melt(id.vars = c("group_id"), na.rm = TRUE) %>%
dcast(group_id ~ variable, value.var = "value", fun.aggregate = max, na.rm = TRUE) %>%
mutate(group_id = gsub("^(\\w+\\s[^,]+)", "\\1,", group_id)) %>%
separate(group_id, into = c("PFAS", "species", "food_web"), sep = ",\\s*", extra = "drop") %>%
mutate(across(ends_with("mean") | ends_with("sd"), ~ as.numeric(as.character(.)))) %>%
mutate(log_PFOS_mean = log10(PFOS_mean))
#write.csv(Koch_2020, file = here("Rdata", "Koch_2020.csv"))
Koch_2020_aquatic <- Koch_2020 %>%
filter(food_web == "aquatic food web") %>%
select_if(~ !all(is.na(.)))
#write.csv(Koch_2020_aquatic, file = here("Rdata", "Koch_2020_aquatic.csv"))
Koch_2020_riparian <- Koch_2020 %>%
filter(food_web == "riparian food web") %>%
select_if(~ !all(is.na(.)))
#write.csv(Koch_2020_riparian, file = here("Rdata", "Koch_2020_riparian.csv"))S_004: Mazzoni_2020
- 2 food webs: including and excluding zooplankton.
- [PFAS] regressed on the original scale. We log-transformed them before running linear models.
- Data were extracted from tables (Table 1 and Table S5).
Mazzoni_2020 <- wide_data %>%
filter(Study_ID == "S_004") %>%
setDT() %>%
melt(id.vars = c("group_id"), na.rm = TRUE) %>%
dcast(group_id ~ variable, value.var = "value", fun.aggregate = max, na.rm = TRUE) %>%
separate(group_id, into = c("PFAS", "food_web", "species"), sep = ",\\s*", extra = "drop") %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.))))
Mazzoni_2020_fw1 <- Mazzoni_2020 %>%
filter(food_web == "food web 1") %>%
mutate(log_PFDA_mean = log10(PFDA_mean)) %>%
mutate(log_PFDoDA_mean = log10(PFDoDA_mean)) %>%
mutate(log_PFNA_mean = log10(PFNA_mean)) %>%
mutate(log_PFOS_mean = log10(PFOS_mean)) %>%
mutate(log_PFUnDA_mean = log10(PFUnDA_mean)) %>%
select_if(~ !all(is.na(.)))
#write.csv(Mazzoni_2020_fw1, file = here("Rdata", "Mazzoni_2020_fw1.csv"))
Mazzoni_2020_fw2 <- Mazzoni_2020 %>%
filter(food_web == "food web 2") %>%
mutate(log_PFDA_mean = log10(PFDA_mean)) %>%
mutate(log_PFDoDA_mean = log10(PFDoDA_mean)) %>%
mutate(log_PFNA_mean = log10(PFNA_mean)) %>%
mutate(log_PFOS_mean = log10(PFOS_mean)) %>%
mutate(log_PFUnDA_mean = log10(PFUnDA_mean)) %>%
select_if(~ !all(is.na(.)))
#write.csv(Mazzoni_2020_fw2, file = here("Rdata", "Mazzoni_2020_fw2.csv"))S_017: Liu_2018
- 1 food web.
- [PFAS] regressed on the original scale. We ln-transformed them before running linear models.
- Data were extracted from tables (Table 1) and plots (Figure 4) using Webplot Digitizer.
Liu_2018 <- wide_data %>%
filter(Study_ID == "S_017") %>%
setDT() %>%
mutate(ln_PFBA_mean = log(PFBA_mean)) %>%
mutate(ln_PFOA_mean = log(PFOA_mean)) %>%
mutate(ln_PFNA_mean = log(PFNA_mean)) %>%
mutate(ln_PFDA_mean = log(PFDA_mean)) %>%
mutate(ln_PFUnDA_mean = log(PFUnDA_mean)) %>%
mutate(ln_PFDoDA_mean = log(PFDoDA_mean)) %>%
mutate(ln_PFTrDA_mean = log(PFTrDA_mean)) %>%
mutate(ln_PFOS_mean = log(PFOS_mean)) %>%
select_if(~ !all(is.na(.)))
#write.csv(Liu_2018, file = here("Rdata", "Liu_2018.csv"))S_019: Gao_2020
- 1 food web.
- [PFAS] already log-tranformed.
- Data were extracted from plots (Figure 4) using metaDigitise.
Gao_2020 <- wide_data %>%
filter(Study_ID == "S_019") %>%
select_if(~ !all(is.na(.))) %>%
setDT()
#write.csv(Gao_2020, file = here("Rdata", "Gao_2020.csv"))S_021: Chen_2018
- 1 food web.
- [PFAS] already ln-tranformed.
- Data were extracted from tables (Table 6).
Chen_2018 <- wide_data %>%
filter(Study_ID == "S_021") %>%
select_if(~ !all(is.na(.))) %>%
setDT()
#write.csv(Chen_2018, file = here("Rdata", "Chen_2018.csv"))S_023: Du_2021
- 1 food web.
- [PFAS] already log-tranformed.
- Data were extracted from plots (Figure 4) using Webplot Digitizer.
Du_2021 <- wide_data %>%
filter(Study_ID == "S_023") %>%
select_if(~ !all(is.na(.))) %>%
setDT()
#write.csv(Du_2021, file = here("Rdata", "Du_2021.csv"))S_035: Ren_2022
- 1 food web: Port Austin (USA), food web including benthic organisms.
- [PFAS] regressed on the original scale. We log-transformed them before running linear models.
- Data were extracted from tables (Table 1).
Ren_2022 <- wide_data %>%
filter(Study_ID == "S_035") %>%
select_if(~ !all(is.na(.))) %>%
setDT() %>%
melt(id.vars = c("group_id"), na.rm = TRUE) %>%
dcast(group_id ~ variable, value.var = "value", fun.aggregate = max, na.rm = TRUE) %>%
separate(group_id, into = c("PFAS", "species"), sep = ",\\s*", extra = "drop") %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.)))) %>%
mutate(log_PFHxS_mean = log10(PFHxS_mean)) %>%
mutate(log_PFOA_mean = log10(PFOA_mean)) %>%
mutate(log_PFOS_mean = log10(PFOS_mean)) %>%
mutate(log_PFNA_mean = log10(PFNA_mean)) %>%
mutate(log_PFDA_mean = log10(PFDA_mean)) %>%
mutate(log_PFUnDA_mean = log10(PFUnDA_mean)) %>%
mutate(log_PFDoDA_mean = log10(PFDoDA_mean)) %>%
mutate(log_PFTrDA_mean = log10(PFTrDA_mean)) %>%
mutate(log_PFTeDA_mean = log10(PFTeDA_mean))
#write.csv(Ren_2022, file = here("Rdata", "Ren_2022.csv"))S_054: Koch_2021
- 2 food webs: freshwater and terrestrial.
- [PFAS] already ln-tranformed.
- [PFAS] regressed against delta15N.
- Data were extracted from plots (Figure S10) using Webplot Digitizer.
Koch_2021 <- wide_data %>%
filter(Study_ID == "S_054") %>%
select_if(~ !all(is.na(.))) %>%
mutate(log_PFOS_mean = log10(PFOS_mean)) %>%
separate(group_id, into = c("PFAS", "food_web", "species"), sep = ",\\s*", extra = "drop") %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.))))
# food web 1 - freshwater
Koch_2021_freshwater <- Koch_2021 %>%
filter(food_web == "food web 1")
#write.csv(Koch_2021_freshwater, file = here("Rdata", "Koch_2021_freshwater.csv"))
# food web 2 - terrestrial
Koch_2021_terrestrial <- Koch_2021 %>%
filter(food_web == "food web 2")
#write.csv(Koch_2021_terrestrial, file = here("Rdata", "Koch_2021_terrestrial.csv"))S_056: Li_2008
- 2 food webs: Tilapia included and excluded.
- [PFAS] already ln-tranformed.
- Data were extracted from plots (Figure 3) using Webplot Digitizer.
Li_2008 <- wide_data %>%
filter(Study_ID == "S_056") %>%
select_if(~ !all(is.na(.))) %>%
separate(group_id, into = c("PFAS", "food_web", "species"), sep = ",\\s*", extra = "drop") %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.))))
# food web 1 - Tilapia excluded
Li_2008_fw1 <- Li_2008 %>%
filter(food_web == "food web 1")
#write.csv(Li_2008_fw1, file = here("Rdata", "Li_2008_fw1.csv"))
# food web 2 - Tilapia included
Li_2008_fw2 <- Li_2008 %>%
filter(food_web == "food web 2")
#write.csv(Li_2008_fw2, file = here("Rdata", "Li_2008_fw2.csv"))S_057: Pan_2010
- 1 food web.
- [PFAS] regressed on the original scale. We log-transformed them before running linear models.
- Data were extracted from plots (Figure S1) using Webplot Digitizer.
Pan_2010 <- wide_data %>%
filter(Study_ID == "S_057") %>%
select_if(~ !all(is.na(.))) %>%
mutate(log_PFCs_mean = log10(PFCs_mean)) %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.))))
#write.csv(Pan_2010, file = here("Rdata", "Pan_2010.csv"))S_058: Van de Vijver_2003
- 1 food web.
- [PFAS] regressed on the original scale. We log-transformed them before running linear models.
- Data were extracted from plots (Figure 3) using Webplot Digitizer.
Van_de_Vijver_2003 <- wide_data %>%
filter(Study_ID == "S_058") %>%
select_if(~ !all(is.na(.))) %>%
mutate(log_PFOS_mean = log10(PFOS_mean)) %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.))))
#write.csv(Van_de_Vijver_2003, file = here("Rdata", "Van_de_Vijver_2003.csv"))S_061: Barghi_2018
- 1 food web.
- [PFAS] regressed on the original scale. We log-transformed them before running linear models.
- Data were extracted from plots (Figure S2) using Webplot Digitizer.
Barghi_2018 <- wide_data %>%
filter(Study_ID == "S_061") %>%
setDT() %>%
select_if(~ !all(is.na(.))) %>%
mutate(log_PFNA_mean = log10(PFNA_mean)) %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.))))
#write.csv(Barghi_2018, file = here("Rdata", "Barghi_2018.csv"))S_060: Kobayashi_2018
- 1 food web.
- [PFAS] on the original scale. We log-transformed them before running linear models.
- Data were extracted from tables (Table 1).
- Standard deviations were log-scaled using the Delta method (Taylor approximation).
Kobayashi_2018 <- wide_data %>%
filter(Study_ID == "S_060") %>%
setDT() %>%
select_if(~ !all(is.na(.))) %>%
mutate(log_PFHxS_mean = log10(PFHxS_mean)) %>%
mutate(log_PFHxS_sd = PFHxS_sd/(PFHxS_mean*log(10))) %>%
mutate(log_PFOS_mean = log10(PFOS_mean)) %>%
mutate(log_PFOS_sd = PFOS_sd/(PFOS_mean*log(10))) %>%
mutate(log_PFPeA_mean = log10(PFPeA_mean)) %>%
mutate(log_PFPeA_sd = PFPeA_sd/(PFPeA_mean*log(10))) %>%
mutate(log_PFHxA_mean = log10(PFHxA_mean)) %>%
mutate(log_PFHxA_sd = PFHxA_sd/(PFHxA_mean*log(10))) %>%
mutate(log_PFHpA_mean = log10(PFHpA_mean)) %>%
mutate(log_PFHpA_sd = PFHpA_sd/(PFHpA_mean*log(10))) %>%
mutate(log_PFOA_mean = log10(PFOA_mean)) %>%
mutate(log_PFOA_sd = PFOA_sd/(PFOA_mean*log(10))) %>%
mutate(log_PFNA_mean = log10(PFNA_mean)) %>%
mutate(log_PFNA_sd = PFNA_sd/(PFNA_mean*log(10))) %>%
mutate(log_PFDA_mean = log10(PFDA_mean)) %>%
mutate(log_PFDA_sd = PFDA_sd/(PFDA_mean*log(10))) %>%
mutate(log_PFUnDA_mean = log10(PFUnDA_mean)) %>%
mutate(log_PFUnDA_sd = PFUnDA_sd/(PFUnDA_mean*log(10))) %>%
mutate(across(ends_with("mean"), ~ as.numeric(as.character(.))))
#write.csv(Kobayashi_2018, file = here("Rdata", "Kobayashi_2018.csv"))S_047: Heimstad_2024
- 1 food web.
- [PFAS] regressed on the original scale, although plots have y-axis on the log scale. We log-transformed them before running linear models.
- Data extracted from plots (Figure S10 - S12) using Webplot Digitizer.
Heimstad_2024 <- wide_data %>%
filter(Study_ID == "S_047") %>%
setDT() %>%
select_if(~ !all(is.na(.))) %>%
mutate(log_PFDoDA_mean = log10(PFDoDA_mean)) %>%
mutate(log_PFOS_mean = log10(PFOS_mean)) %>%
mutate(log_PFUnDA_mean = log10(PFUnDA_mean))
# write.csv(Heimstad_2024, file = here("Rdata", "Heimstad_2024.csv"))S_024: Li_2021
- 1 food web.
- [PFAS] already log-tranformed.
- [PFAS] regressed against trophic levels.
- Data were extracted from plots (Figure 4, Figure S7, Figure S8) using Webplot Digitizer.
Li_2021 <- wide_data %>%
filter(Study_ID == "S_024") %>%
setDT() %>%
select_if(~ !all(is.na(.)))
#write.csv(Li_2021, file = here("Rdata", "Li_2021.csv"))S_025: Pan_2021
- 1 food web.
- [PFAS] already log-tranformed.
- [PFAS] regressed against trophic levels.
- Data were extracted from plots (Figure 5) using Webplot Digitizer.
Pan_2021 <- wide_data %>%
filter(Study_ID == "S_025") %>%
setDT() %>%
select_if(~ !all(is.na(.)))
#write.csv(Pan_2021, file = here("Rdata", "Pan_2021.csv"))S_059: Jiao_2023
- 1 food web.
- [PFAS] measured in several tissues.
- [PFAS] already ln-tranformed.
- [PFAS] regressed against trophic levels.
- Data were extracted from plots (Figure 4c) using Webplot Digitizer.
Jiao_2023 <- wide_data %>%
filter(Study_ID == "S_059") %>%
setDT() %>%
separate(group_id, into = c("PFAS", "tissue", "species"), sep = ",\\s*", extra = "drop") %>%
mutate(across(where(is.character), ~na_if(., ""))) %>%
select_if(~ !all(is.na(.)))
#write.csv(Jiao_2023, file = here("Rdata", "Jiao_2023.csv"))
# Divide the Jiao_2023 dataset into tissues.
Jiao_2023_blood <- Jiao_2023 %>%
filter(tissue == "blood")
#write.csv(Jiao_2023_blood, file = here("Rdata", "Jiao_2023_blood.csv"))
Jiao_2023_gill <- Jiao_2023 %>%
filter(tissue == "gill")
#write.csv(Jiao_2023_gill, file = here("Rdata", "Jiao_2023_gill.csv"))
Jiao_2023_heart <- Jiao_2023 %>%
filter(tissue == "heart")
#write.csv(Jiao_2023_heart, file = here("Rdata", "Jiao_2023_heart.csv"))
Jiao_2023_liver <- Jiao_2023 %>%
filter(tissue == "liver")
#write.csv(Jiao_2023_liver, file = here("Rdata", "Jiao_2023_liver.csv"))
Jiao_2023_kidney <- Jiao_2023 %>%
filter(tissue == "kidney")
#write.csv(Jiao_2023_kidney, file = here("Rdata", "Jiao_2023_kidney.csv"))
Jiao_2023_pancreas <- Jiao_2023 %>%
filter(tissue == "pancreas")
#write.csv(Jiao_2023_pancreas, file = here("Rdata", "Jiao_2023_pancreas.csv"))
Jiao_2023_muscle <- Jiao_2023 %>%
filter(tissue == "muscle")
#write.csv(Jiao_2023_muscle, file = here("Rdata", "Jiao_2023_muscle.csv"))Linear models
S_052: Cara_2022
- It provides standard deviations of individual observations. Weights were assigned (inverse of square standard deviation).
First food web - “location 4”
Cara_2022_loc4 <- read.csv(here("RData", "Cara_2022_loc4.csv"))
Cara_2022_loc4_PFOA_weights <- 1 / (Cara_2022_loc4$log_PFOA_sd^2) # weights as the inverse of square standard deviation. Please note we are taking the assumption that n is exactly the same for each observation. This is an assumption that must be taken in order to use the standard deviation of observations as weights.
Cara_2022_loc4_lm_model_PFOA <- lm(log_PFOA_mean ~ TL_mean,
data = Cara_2022_loc4,
weights = Cara_2022_loc4_PFOA_weights)
Cara_2022_loc4_PFOA_slope <- coef(Cara_2022_loc4_lm_model_PFOA)["TL_mean"]
#plot(residuals(Cara_2022_loc4_lm_model_PFOA))
#coeftest(Cara_2022_loc4_lm_model_PFOA, vcov. = vcovHC)
# or
summary(Cara_2022_loc4_lm_model_PFOA)##
## Call:
## lm(formula = log_PFOA_mean ~ TL_mean, data = Cara_2022_loc4,
## weights = Cara_2022_loc4_PFOA_weights)
##
## Weighted Residuals:
## 1 2 3 4 5 6
## 0.7261 1.0776 0.6497 0.5281 -1.1778 0.7603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.56663 0.24447 2.318 0.0813 .
## TL_mean -0.16502 0.06941 -2.378 0.0762 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.043 on 4 degrees of freedom
## Multiple R-squared: 0.5856, Adjusted R-squared: 0.482
## F-statistic: 5.653 on 1 and 4 DF, p-value: 0.07619ggplot(Cara_2022_loc4, aes(x = TL_mean, y = log_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Cara_2022_loc4_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Second food web - “location 5”
Cara_2022_loc5 <- read.csv(here("RData", "Cara_2022_loc5.csv"))
Cara_2022_loc5_PFOA_weights <- 1 / (Cara_2022_loc5$log_PFOA_sd^2)
Cara_2022_loc5_PFUnDA_weights <- 1 / (Cara_2022_loc5$log_PFUnDA_sd^2)
Cara_2022_loc5_lm_model_PFOA <- lm(log_PFOA_mean ~ TL_mean,
data = Cara_2022_loc5,
weights = Cara_2022_loc5_PFOA_weights)
Cara_2022_loc5_PFOA_slope <- coef(Cara_2022_loc5_lm_model_PFOA)["TL_mean"]
summary(Cara_2022_loc5_lm_model_PFOA)##
## Call:
## lm(formula = log_PFOA_mean ~ TL_mean, data = Cara_2022_loc5,
## weights = Cara_2022_loc5_PFOA_weights)
##
## Weighted Residuals:
## 1 2 3 4 5
## 0.06248 1.02125 -0.86871 0.28294 0.65292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3442 0.1530 2.250 0.110
## TL_mean -0.1045 0.0483 -2.163 0.119
##
## Residual standard error: 0.8771 on 3 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.6092, Adjusted R-squared: 0.479
## F-statistic: 4.677 on 1 and 3 DF, p-value: 0.1193ggplot(Cara_2022_loc5, aes(x = TL_mean, y = log_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Cara_2022_loc5_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Cara_2022_loc5_lm_model_PFUnDA <- lm(log_PFUnDA_mean ~ TL_mean,
data = Cara_2022_loc5,
weights = Cara_2022_loc5_PFUnDA_weights)
Cara_2022_loc5_PFUnDA_slope <- coef(Cara_2022_loc5_lm_model_PFUnDA)["TL_mean"]
summary(Cara_2022_loc5_lm_model_PFUnDA)##
## Call:
## lm(formula = log_PFUnDA_mean ~ TL_mean, data = Cara_2022_loc5,
## weights = Cara_2022_loc5_PFUnDA_weights)
##
## Weighted Residuals:
## 6 7 8 9 10
## 0.008169 0.420248 -0.768225 0.084596 0.266768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.47930 0.19671 2.437 0.0928 .
## TL_mean -0.24555 0.06287 -3.906 0.0298 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5308 on 3 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.8357, Adjusted R-squared: 0.7809
## F-statistic: 15.25 on 1 and 3 DF, p-value: 0.02981ggplot(Cara_2022_loc5, aes(x = TL_mean, y = log_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Cara_2022_loc5_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Third food web - “location 8”
Cara_2022_loc8 <- read.csv(here("RData", "Cara_2022_loc8.csv"))
Cara_2022_loc8_PFOA_weights <- 1 / (Cara_2022_loc8$log_PFOA_sd^2)
Cara_2022_loc8_PFTrDA_weights <- 1 / (Cara_2022_loc8$log_PFTrDA_sd^2)
Cara_2022_loc8_lm_model_PFOA <- lm(log_PFOA_mean ~ TL_mean,
data = Cara_2022_loc8,
weights = Cara_2022_loc8_PFOA_weights)
Cara_2022_loc8_PFOA_slope <- coef(Cara_2022_loc8_lm_model_PFOA)["TL_mean"]
summary(Cara_2022_loc8_lm_model_PFOA)##
## Call:
## lm(formula = log_PFOA_mean ~ TL_mean, data = Cara_2022_loc8,
## weights = Cara_2022_loc8_PFOA_weights)
##
## Weighted Residuals:
## 1 2 3 4 5 6
## 1.5878 1.4728 -1.5827 0.3825 0.1319 0.2770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.30025 0.28777 1.043 0.356
## TL_mean -0.09357 0.09254 -1.011 0.369
##
## Residual standard error: 1.363 on 4 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.2036, Adjusted R-squared: 0.004454
## F-statistic: 1.022 on 1 and 4 DF, p-value: 0.3692ggplot(Cara_2022_loc8, aes(x = TL_mean, y = log_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Cara_2022_loc8_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Cara_2022_loc8_lm_model_PFTrDA <- lm(log_PFTrDA_mean ~ TL_mean,
data = Cara_2022_loc8,
weights = Cara_2022_loc8_PFTrDA_weights)
Cara_2022_loc8_PFTrDA_slope <- coef(Cara_2022_loc8_lm_model_PFTrDA)["TL_mean"]
summary(Cara_2022_loc8_lm_model_PFTrDA)##
## Call:
## lm(formula = log_PFTrDA_mean ~ TL_mean, data = Cara_2022_loc8,
## weights = Cara_2022_loc8_PFTrDA_weights)
##
## Weighted Residuals:
## 7 8 9 10 11
## -0.2362 -0.3168 0.2648 0.5207 -0.2130
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.81953 0.20726 -23.25 0.000174 ***
## TL_mean 0.97941 0.06286 15.58 0.000574 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4253 on 3 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.9878, Adjusted R-squared: 0.9837
## F-statistic: 242.8 on 1 and 3 DF, p-value: 0.0005745ggplot(Cara_2022_loc8, aes(x = TL_mean, y = log_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Cara_2022_loc8_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_013: Martin_2004
- It provides standard errors of individual observations. Weights were assigned (inverse of standard error).
Benthic food web
WARNING! Benthic food web includes only two trophic levels! It cannot be considered a food web.
SE values for benthic food web slope cannot be calculated.
Martin_2004_benthic <- read.csv(here("RData/Martin_2004_benthic.csv"))
Martin_2004_benthic_PFOS_weights <- 1 / Martin_2004_benthic$se^2 # weights as the inverse of standard error
# PFOS
Martin_2004_benthic_lm_model_PFOS <- lm(ln_PFOS_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFOS_slope <- coef(Martin_2004_benthic_lm_model_PFOS)["TL_mean"]
#plot(residuals(Martin_2004_benthic_lm_model_PFOS))
summary(Martin_2004_benthic_lm_model_PFOS)##
## Call:
## lm(formula = ln_PFOS_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.5179 NaN NaN NaN
## TL_mean 0.6685 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOA
Martin_2004_benthic_lm_model_PFOA <- lm(ln_PFOA_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFOA_slope <- coef(Martin_2004_benthic_lm_model_PFOA)["TL_mean"]
summary(Martin_2004_benthic_lm_model_PFOA)##
## Call:
## lm(formula = ln_PFOA_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7887 NaN NaN NaN
## TL_mean -0.9692 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFNA
Martin_2004_benthic_lm_model_PFNA <- lm(ln_PFNA_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFNA_slope <- coef(Martin_2004_benthic_lm_model_PFNA)["TL_mean"]
summary(Martin_2004_benthic_lm_model_PFNA)##
## Call:
## lm(formula = ln_PFNA_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.465 NaN NaN NaN
## TL_mean -1.297 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFNA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFNA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFNA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDA
Martin_2004_benthic_lm_model_PFDA <- lm(ln_PFDA_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFDA_slope <- coef(Martin_2004_benthic_lm_model_PFDA)["TL_mean"]
summary(Martin_2004_benthic_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.9059 NaN NaN NaN
## TL_mean -0.1668 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Martin_2004_benthic_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFUnDA_slope <- coef(Martin_2004_benthic_lm_model_PFUnDA)["TL_mean"]
summary(Martin_2004_benthic_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.08783 NaN NaN NaN
## TL_mean -0.02744 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Martin_2004_benthic_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFDoDA_slope <- coef(Martin_2004_benthic_lm_model_PFDoDA)["TL_mean"]
summary(Martin_2004_benthic_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.31728 NaN NaN NaN
## TL_mean 0.01814 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Martin_2004_benthic_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFTrDA_slope <- coef(Martin_2004_benthic_lm_model_PFTrDA)["TL_mean"]
summary(Martin_2004_benthic_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.6146 NaN NaN NaN
## TL_mean -0.1779 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTeDA
Martin_2004_benthic_lm_model_PFTeDA <- lm(ln_PFTeDA_mean ~ TL_mean,
data = Martin_2004_benthic,
weights = Martin_2004_benthic_PFOS_weights)
Martin_2004_benthic_PFTeDA_slope <- coef(Martin_2004_benthic_lm_model_PFTeDA)["TL_mean"]
summary(Martin_2004_benthic_lm_model_PFTeDA)##
## Call:
## lm(formula = ln_PFTeDA_mean ~ TL_mean, data = Martin_2004_benthic,
## weights = Martin_2004_benthic_PFOS_weights)
##
## Weighted Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.083 NaN NaN NaN
## TL_mean -0.846 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NAggplot(Martin_2004_benthic, aes(x = TL_mean, y = ln_PFTeDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFTeDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_benthic_PFTeDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Pelagic food web
Martin_2004_pelagic <- read.csv(here("RData", "Martin_2004_pelagic.csv"))
Martin_2004_pelagic_weights <- Martin_2004_pelagic$se^2
# PFOS
Martin_2004_pelagic_lm_model_PFOS <- lm(ln_PFOS_mean ~ TL_mean,
data = Martin_2004_pelagic,
weights = Martin_2004_pelagic_weights)
Martin_2004_pelagic_PFOS_slope <- coef(Martin_2004_pelagic_lm_model_PFOS)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFOS)##
## Call:
## lm(formula = ln_PFOS_mean ~ TL_mean, data = Martin_2004_pelagic,
## weights = Martin_2004_pelagic_weights)
##
## Weighted Residuals:
## 13 14 15 16
## -0.06550 -0.03859 0.09522 -0.05332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.4564 1.6388 -5.770 0.0287 *
## TL_mean 1.5708 0.3656 4.296 0.0501 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09405 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.9022, Adjusted R-squared: 0.8533
## F-statistic: 18.46 on 1 and 2 DF, p-value: 0.05014ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOA
Martin_2004_pelagic_lm_model_PFOA <- lm(ln_PFOA_mean ~ TL_mean,
data = Martin_2004_pelagic,
weights = Martin_2004_pelagic_weights)
Martin_2004_pelagic_PFOA_slope <- coef(Martin_2004_pelagic_lm_model_PFOA)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFOA)##
## Call:
## lm(formula = ln_PFOA_mean ~ TL_mean, data = Martin_2004_pelagic,
## weights = Martin_2004_pelagic_weights)
##
## Weighted Residuals:
## 9 10 11 12
## 0.08925 -0.07673 0.05630 -0.02587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.549168 2.078773 -3.15 0.0877 .
## TL_mean 0.004784 0.492038 0.01 0.9931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09405 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 4.727e-05, Adjusted R-squared: -0.4999
## F-statistic: 9.454e-05 on 1 and 2 DF, p-value: 0.9931ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFNA
Martin_2004_pelagic_lm_model_PFNA <- lm(ln_PFNA_mean ~ TL_mean,
data = Martin_2004_pelagic,
weights = Martin_2004_pelagic_weights)
Martin_2004_pelagic_PFNA_slope <- coef(Martin_2004_pelagic_lm_model_PFNA)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFNA)##
## Call:
## lm(formula = ln_PFNA_mean ~ TL_mean, data = Martin_2004_pelagic,
## weights = Martin_2004_pelagic_weights)
##
## Weighted Residuals:
## 5 6 7 8
## 0.2937 -0.3944 0.9441 -0.2643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.073 6.190 -2.758 0.110
## TL_mean 2.367 1.409 1.680 0.235
##
## Residual standard error: 0.7755 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.5852, Adjusted R-squared: 0.3778
## F-statistic: 2.822 on 1 and 2 DF, p-value: 0.235ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFNA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFNA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFNA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDA
Martin_2004_pelagic_lm_model_PFDA <- lm(ln_PFDA_mean ~ TL_mean,
data = Martin_2004_pelagic,
weights = Martin_2004_pelagic_weights)
Martin_2004_pelagic_PFDA_slope <- coef(Martin_2004_pelagic_lm_model_PFDA)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ TL_mean, data = Martin_2004_pelagic,
## weights = Martin_2004_pelagic_weights)
##
## Weighted Residuals:
## 1 2 3 4
## 0.06523 -0.19557 0.41969 -0.11900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.9428 3.7142 -3.754 0.0642 .
## TL_mean 1.7900 0.8325 2.150 0.1645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3412 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.6981, Adjusted R-squared: 0.5471
## F-statistic: 4.624 on 1 and 2 DF, p-value: 0.1645ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Martin_2004_pelagic_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ TL_mean,
data = Martin_2004_pelagic,
weights = Martin_2004_pelagic_weights)
Martin_2004_pelagic_PFUnDA_slope <- coef(Martin_2004_pelagic_lm_model_PFUnDA)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ TL_mean, data = Martin_2004_pelagic,
## weights = Martin_2004_pelagic_weights)
##
## Weighted Residuals:
## 29 30 31 32
## 0.1800 -0.2403 0.4952 -0.1082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15.8498 3.7612 -4.214 0.052 .
## TL_mean 2.2257 0.8449 2.634 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4166 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.7763, Adjusted R-squared: 0.6644
## F-statistic: 6.94 on 1 and 2 DF, p-value: 0.1189ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Martin_2004_pelagic_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ TL_mean,
data = Martin_2004_pelagic,
weights = Martin_2004_pelagic_weights)
Martin_2004_pelagic_PFDoDA_slope <- coef(Martin_2004_pelagic_lm_model_PFDoDA)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ TL_mean, data = Martin_2004_pelagic,
## weights = Martin_2004_pelagic_weights)
##
## Weighted Residuals:
## 25 26 27 28
## 0.06900 -0.06292 0.21296 -0.03555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.2005 1.5045 -6.780 0.0211 *
## TL_mean 0.9132 0.3438 2.656 0.1173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1663 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.7791, Adjusted R-squared: 0.6687
## F-statistic: 7.055 on 1 and 2 DF, p-value: 0.1173ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Martin_2004_pelagic_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ TL_mean,
data = Martin_2004_pelagic,
weights = Martin_2004_pelagic_weights)
Martin_2004_pelagic_PFTrDA_slope <- coef(Martin_2004_pelagic_lm_model_PFTrDA)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ TL_mean, data = Martin_2004_pelagic,
## weights = Martin_2004_pelagic_weights)
##
## Weighted Residuals:
## 21 22 23 24
## -0.001384 -0.091328 0.079173 -0.022628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.3035 1.7723 -5.249 0.0344 *
## TL_mean 0.7857 0.3790 2.073 0.1739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08696 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.6825, Adjusted R-squared: 0.5237
## F-statistic: 4.299 on 1 and 2 DF, p-value: 0.1739ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTeDA
Martin_2004_pelagic_lm_model_PFTeDA <- lm(ln_PFTeDA_mean ~ TL_mean,
data = Martin_2004_pelagic)
Martin_2004_pelagic_PFTeDA_slope <- coef(Martin_2004_pelagic_lm_model_PFTeDA)["TL_mean"]
summary(Martin_2004_pelagic_lm_model_PFTeDA)##
## Call:
## lm(formula = ln_PFTeDA_mean ~ TL_mean, data = Martin_2004_pelagic)
##
## Residuals:
## 17 18 19 20
## 0.6875 -0.9092 -0.3270 0.5487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.0291 4.0316 -1.495 0.273
## TL_mean -0.3242 0.9409 -0.345 0.763
##
## Residual standard error: 0.9239 on 2 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.05603, Adjusted R-squared: -0.4159
## F-statistic: 0.1187 on 1 and 2 DF, p-value: 0.7633ggplot(Martin_2004_pelagic, aes(x = TL_mean, y = ln_PFTeDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFTeDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_pelagic_PFTeDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Whole food web
Martin_2004_whole <- read.csv(here("RData", "Martin_2004_whole.csv"))
Martin_2004_whole_FOSA_weights <- 1 / Martin_2004_whole$se^2
Martin_2004_whole_lm_model_FOSA <- lm(ln_FOSA_mean ~ TL_mean,
data = Martin_2004_whole,
weights = Martin_2004_whole_FOSA_weights)
Martin_2004_whole_FOSA_slope <- coef(Martin_2004_whole_lm_model_FOSA)["TL_mean"]
summary(Martin_2004_whole_lm_model_FOSA)##
## Call:
## lm(formula = ln_FOSA_mean ~ TL_mean, data = Martin_2004_whole,
## weights = Martin_2004_whole_FOSA_weights)
##
## Weighted Residuals:
## 1 2 3 4 5 6
## 1.556 -1.913 1.144 -5.478 -2.185 -2.248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.79003 1.78492 -1.003 0.373
## TL_mean -0.03605 0.45095 -0.080 0.940
##
## Residual standard error: 3.436 on 4 degrees of freedom
## Multiple R-squared: 0.001595, Adjusted R-squared: -0.248
## F-statistic: 0.006391 on 1 and 4 DF, p-value: 0.9401ggplot(Martin_2004_whole, aes(x = TL_mean, y = ln_FOSA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [FOSA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Martin_2004_whole_FOSA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_053: Koch_2020
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Aquatic food web
Koch_2020_aquatic <- read.csv(here("Rdata", "Koch_2020_aquatic.csv"))
Koch_2020_aquatic_lm_model_PFOS <- lm(log_PFOS_mean ~ delta_15N_mean,
data = Koch_2020_aquatic)
Koch_2020_aquatic_PFOS_slope <- coef(Koch_2020_aquatic_lm_model_PFOS)["delta_15N_mean"]
summary(Koch_2020_aquatic_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ delta_15N_mean, data = Koch_2020_aquatic)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0713 -0.2352 0.0970 0.3110 0.5489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.58222 0.55873 4.622 0.000479 ***
## delta_15N_mean 0.06616 0.04848 1.365 0.195451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4431 on 13 degrees of freedom
## Multiple R-squared: 0.1253, Adjusted R-squared: 0.05806
## F-statistic: 1.863 on 1 and 13 DF, p-value: 0.1955ggplot(Koch_2020_aquatic, aes(x = delta_15N_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Koch_2020_aquatic_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Riparian food web
Koch_2020_riparian <- read.csv(here("Rdata", "Koch_2020_riparian.csv"))
Koch_2020_riparian_lm_model_PFOS <- lm(log_PFOS_mean ~ delta_15N_mean,
data = Koch_2020_riparian)
Koch_2020_riparian_PFOS_slope <- coef(Koch_2020_riparian_lm_model_PFOS)["delta_15N_mean"]
summary(Koch_2020_riparian_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ delta_15N_mean, data = Koch_2020_riparian)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3215 -0.2697 0.1286 0.3197 1.1980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.72084 0.39533 1.823 0.09132 .
## delta_15N_mean 0.21637 0.05867 3.688 0.00273 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.624 on 13 degrees of freedom
## Multiple R-squared: 0.5113, Adjusted R-squared: 0.4737
## F-statistic: 13.6 on 1 and 13 DF, p-value: 0.002733ggplot(Koch_2020_riparian, aes(x = delta_15N_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Koch_2020_riparian_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_004: Mazzoni_2020
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Food web 1 - including zooplankton
Mazzoni_2020_fw1 <- read.csv(here("Rdata", "Mazzoni_2020_fw1.csv"))
# PFDA
Mazzoni_2020_fw1_lm_model_PFDA <- lm(log_PFDA_mean ~ TL_mean,
data = Mazzoni_2020_fw1)
Mazzoni_2020_fw1_PFDA_slope <- coef(Mazzoni_2020_fw1_lm_model_PFDA)["TL_mean"]
summary(Mazzoni_2020_fw1_lm_model_PFDA)##
## Call:
## lm(formula = log_PFDA_mean ~ TL_mean, data = Mazzoni_2020_fw1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93185 -0.20709 0.05011 0.19154 0.72071
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.2364 0.6283 -3.559 0.00241 **
## TL_mean 0.4641 0.1304 3.558 0.00242 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3684 on 17 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.4269, Adjusted R-squared: 0.3932
## F-statistic: 12.66 on 1 and 17 DF, p-value: 0.002418ggplot(Mazzoni_2020_fw1, aes(x = TL_mean, y = log_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw1_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Mazzoni_2020_fw1_lm_model_PFDoDA <- lm(log_PFDoDA_mean ~ TL_mean,
data = Mazzoni_2020_fw1)
Mazzoni_2020_fw1_PFDoDA_slope <- coef(Mazzoni_2020_fw1_lm_model_PFDoDA)["TL_mean"]
summary(Mazzoni_2020_fw1_lm_model_PFDoDA)##
## Call:
## lm(formula = log_PFDoDA_mean ~ TL_mean, data = Mazzoni_2020_fw1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3739 -0.2267 0.1303 0.3911 0.7247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.38106 0.94848 -0.402 0.693
## TL_mean 0.06492 0.19489 0.333 0.743
##
## Residual standard error: 0.5659 on 17 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.006484, Adjusted R-squared: -0.05196
## F-statistic: 0.111 on 1 and 17 DF, p-value: 0.7431ggplot(Mazzoni_2020_fw1, aes(x = TL_mean, y = log_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw1_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFNA
Mazzoni_2020_fw1_lm_model_PFNA <- lm(log_PFNA_mean ~ TL_mean,
data = Mazzoni_2020_fw1)
Mazzoni_2020_fw1_PFNA_slope <- coef(Mazzoni_2020_fw1_lm_model_PFNA)["TL_mean"]
summary(Mazzoni_2020_fw1_lm_model_PFNA)##
## Call:
## lm(formula = log_PFNA_mean ~ TL_mean, data = Mazzoni_2020_fw1)
##
## Residuals:
## 47 52 53 58 59
## -0.05068 0.07628 0.26401 -0.39392 0.10431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3214 1.6166 -0.199 0.855
## TL_mean -0.1055 0.3343 -0.316 0.773
##
## Residual standard error: 0.2853 on 3 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: 0.03214, Adjusted R-squared: -0.2905
## F-statistic: 0.09963 on 1 and 3 DF, p-value: 0.773ggplot(Mazzoni_2020_fw1, aes(x = TL_mean, y = log_PFNA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFNA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw1_PFNA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS - Note: The study already provides the TMS and SE for this compound
Mazzoni_2020_fw1_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean, data = Mazzoni_2020_fw1)
Mazzoni_2020_fw1_PFOS_slope <- coef(Mazzoni_2020_fw1_lm_model_PFOS)["TL_mean"]
summary(Mazzoni_2020_fw1_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Mazzoni_2020_fw1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.49684 -0.18993 0.08087 0.38715 0.79660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7305 0.9551 -1.812 0.0867 .
## TL_mean 0.4834 0.1968 2.457 0.0244 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5729 on 18 degrees of freedom
## (80 observations deleted due to missingness)
## Multiple R-squared: 0.2511, Adjusted R-squared: 0.2095
## F-statistic: 6.036 on 1 and 18 DF, p-value: 0.0244ggplot(Mazzoni_2020_fw1, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw1_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Mazzoni_2020_fw1_lm_model_PFUnDA <- lm(log_PFUnDA_mean ~ TL_mean,
data = Mazzoni_2020_fw1)
Mazzoni_2020_fw1_PFUnDA_slope <- coef(Mazzoni_2020_fw1_lm_model_PFUnDA)["TL_mean"]
summary(Mazzoni_2020_fw1_lm_model_PFUnDA)##
## Call:
## lm(formula = log_PFUnDA_mean ~ TL_mean, data = Mazzoni_2020_fw1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5231 -0.1691 0.1004 0.2885 0.7538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.26377 0.81992 -0.322 0.751
## TL_mean 0.08834 0.16892 0.523 0.607
##
## Residual standard error: 0.4918 on 18 degrees of freedom
## (80 observations deleted due to missingness)
## Multiple R-squared: 0.01497, Adjusted R-squared: -0.03976
## F-statistic: 0.2735 on 1 and 18 DF, p-value: 0.6074ggplot(Mazzoni_2020_fw1, aes(x = TL_mean, y = log_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw1_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Food web 2 - excluding zooplankton
Mazzoni_2020_fw2 <- read.csv(here("Rdata", "Mazzoni_2020_fw2.csv"))
# PFDA
Mazzoni_2020_fw2_lm_model_PFDA <- lm(log_PFDA_mean ~ TL_mean,
data = Mazzoni_2020_fw2)
Mazzoni_2020_fw2_PFDA_slope <- coef(Mazzoni_2020_fw2_lm_model_PFDA)["TL_mean"]
summary(Mazzoni_2020_fw2_lm_model_PFDA)##
## Call:
## lm(formula = log_PFDA_mean ~ TL_mean, data = Mazzoni_2020_fw2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.55706 -0.30415 0.05658 0.21234 0.68616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0074 0.5799 -1.737 0.1015
## TL_mean 0.2216 0.1203 1.842 0.0841 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.344 on 16 degrees of freedom
## (58 observations deleted due to missingness)
## Multiple R-squared: 0.175, Adjusted R-squared: 0.1234
## F-statistic: 3.393 on 1 and 16 DF, p-value: 0.08409ggplot(Mazzoni_2020_fw2, aes(x = TL_mean, y = log_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw2_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Mazzoni_2020_fw2_lm_model_PFDoDA <- lm(log_PFDoDA_mean ~ TL_mean,
data = Mazzoni_2020_fw2)
Mazzoni_2020_fw2_PFDoDA_slope <- coef(Mazzoni_2020_fw2_lm_model_PFDoDA)["TL_mean"]
summary(Mazzoni_2020_fw2_lm_model_PFDoDA)##
## Call:
## lm(formula = log_PFDoDA_mean ~ TL_mean, data = Mazzoni_2020_fw2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3998 -0.1265 0.1572 0.3727 0.6608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.49258 0.95503 -0.516 0.613
## TL_mean 0.09328 0.19743 0.472 0.643
##
## Residual standard error: 0.5721 on 16 degrees of freedom
## (58 observations deleted due to missingness)
## Multiple R-squared: 0.01376, Adjusted R-squared: -0.04788
## F-statistic: 0.2232 on 1 and 16 DF, p-value: 0.643ggplot(Mazzoni_2020_fw2, aes(x = TL_mean, y = log_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw2_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Mazzoni_2020_fw2_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Mazzoni_2020_fw2)
Mazzoni_2020_fw2_PFOS_slope <- coef(Mazzoni_2020_fw2_lm_model_PFOS)["TL_mean"]
summary(Mazzoni_2020_fw2_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Mazzoni_2020_fw2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.28863 -0.19577 0.04808 0.39692 0.84789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40479 0.84730 0.478 0.639
## TL_mean 0.05947 0.17484 0.340 0.738
##
## Residual standard error: 0.5079 on 17 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.00676, Adjusted R-squared: -0.05167
## F-statistic: 0.1157 on 1 and 17 DF, p-value: 0.7379ggplot(Mazzoni_2020_fw2, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw2_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Mazzoni_2020_fw2_lm_model_PFUnDA <- lm(log_PFUnDA_mean ~ TL_mean,
data = Mazzoni_2020_fw2)
Mazzoni_2020_fw2_PFUnDA_slope <- coef(Mazzoni_2020_fw2_lm_model_PFUnDA)["TL_mean"]
summary(Mazzoni_2020_fw2_lm_model_PFUnDA)##
## Call:
## lm(formula = log_PFUnDA_mean ~ TL_mean, data = Mazzoni_2020_fw2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.59708 -0.08981 0.15726 0.25468 0.60714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.9283 0.7915 -1.173 0.257
## TL_mean 0.2310 0.1633 1.414 0.175
##
## Residual standard error: 0.4745 on 17 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.1053, Adjusted R-squared: 0.05267
## F-statistic: 2.001 on 1 and 17 DF, p-value: 0.1753ggplot(Mazzoni_2020_fw2, aes(x = TL_mean, y = log_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Mazzoni_2020_fw2_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_017: Liu_2018
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Liu_2018 <- read.csv(here("Rdata", "Liu_2018.csv"))
# PFBA
Liu_2018_lm_model_PFBA <- lm(ln_PFBA_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFBA_slope <- coef(Liu_2018_lm_model_PFBA)["TL_mean"]
summary(Liu_2018_lm_model_PFBA)##
## Call:
## lm(formula = ln_PFBA_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 1 2 3 4 5 6
## -0.24536 -0.52656 0.03412 -0.38730 -0.57306 1.69816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.0793 2.6240 -0.792 0.472
## TL_mean 2.4606 0.9403 2.617 0.059 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9619 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.5391
## F-statistic: 6.848 on 1 and 4 DF, p-value: 0.05899ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFBA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFBA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFBA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOA
Liu_2018_lm_model_PFOA <- lm(ln_PFOA_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFOA_slope <- coef(Liu_2018_lm_model_PFOA)["TL_mean"]
summary(Liu_2018_lm_model_PFOA)##
## Call:
## lm(formula = ln_PFOA_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 7 8 9 10 11 12
## 1.07869 -0.11081 -0.90866 -0.82724 0.75485 0.01316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1811 2.4627 0.480 0.657
## TL_mean 0.6875 0.8825 0.779 0.479
##
## Residual standard error: 0.9022 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.1317, Adjusted R-squared: -0.08534
## F-statistic: 0.6068 on 1 and 4 DF, p-value: 0.4795ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFNA
Liu_2018_lm_model_PFNA <- lm(ln_PFNA_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFNA_slope <- coef(Liu_2018_lm_model_PFNA)["TL_mean"]
summary(Liu_2018_lm_model_PFNA)##
## Call:
## lm(formula = ln_PFNA_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 13 14 15 16 17 18
## 0.66783 0.06036 -0.53389 -0.27854 -0.07326 0.15750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5251 1.2654 0.415 0.6994
## TL_mean 1.2805 0.4542 2.819 0.0479 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4589 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.6652, Adjusted R-squared: 0.5815
## F-statistic: 7.949 on 1 and 4 DF, p-value: 0.04786ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFNA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFNA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFNA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDA
Liu_2018_lm_model_PFDA <- lm(ln_PFDA_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFDA_slope <- coef(Liu_2018_lm_model_PFDA)["TL_mean"]
summary(Liu_2018_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 19 20 21 22 23 24
## -0.06855 -0.18741 0.03599 -0.11689 0.50385 -0.16698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6697 0.7906 0.847 0.44468
## TL_mean 1.7012 0.2830 6.011 0.00386 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2901 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.9003, Adjusted R-squared: 0.8754
## F-statistic: 36.13 on 1 and 4 DF, p-value: 0.003857ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Liu_2018_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFUnDA_slope <- coef(Liu_2018_lm_model_PFUnDA)["TL_mean"]
summary(Liu_2018_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 25 26 27 28 29 30
## -0.4596 -0.1948 0.3587 -0.1544 0.7396 -0.2894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1569 1.3781 0.114 0.9148
## TL_mean 1.8537 0.4951 3.744 0.0201 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5081 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.778, Adjusted R-squared: 0.7225
## F-statistic: 14.02 on 1 and 4 DF, p-value: 0.02006ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Liu_2018_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFDoDA_slope <- coef(Liu_2018_lm_model_PFDoDA)["TL_mean"]
summary(Liu_2018_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 31 32 33 34 35 36
## -0.5404 -0.5503 0.9968 -0.2905 0.6158 -0.2314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2277 1.9814 0.115 0.9140
## TL_mean 1.5351 0.7095 2.164 0.0965 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7255 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.5392, Adjusted R-squared: 0.4241
## F-statistic: 4.681 on 1 and 4 DF, p-value: 0.09649ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Liu_2018_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFTrDA_slope <- coef(Liu_2018_lm_model_PFTrDA)["TL_mean"]
summary(Liu_2018_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 37 38 39 40 41 42
## -0.5508 -0.5092 0.9813 -0.3004 0.6191 -0.2400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2900 1.9285 0.150 0.8877
## TL_mean 1.5170 0.6929 2.189 0.0938 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7171 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.5451, Adjusted R-squared: 0.4314
## F-statistic: 4.794 on 1 and 4 DF, p-value: 0.09375ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Liu_2018_lm_model_PFOS <- lm(ln_PFOS_mean ~ TL_mean,
data = Liu_2018)
Liu_2018_PFOS_slope <- coef(Liu_2018_lm_model_PFOS)["TL_mean"]
summary(Liu_2018_lm_model_PFOS)##
## Call:
## lm(formula = ln_PFOS_mean ~ TL_mean, data = Liu_2018)
##
## Residuals:
## 43 44 45 46 47 48
## -0.70950 0.08285 0.29514 0.08153 0.54284 -0.29286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9200 1.3471 -1.425 0.22721
## TL_mean 2.3235 0.4834 4.806 0.00861 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4961 on 4 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.8524, Adjusted R-squared: 0.8155
## F-statistic: 23.1 on 1 and 4 DF, p-value: 0.008608ggplot(Liu_2018, aes(x = TL_mean, y = ln_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Liu_2018_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_019: Gao_2020
- It provides standard deviations of individual observations. Weights were assigned (inverse of square standard deviation).
Gao_2020 <- read.csv(here("Rdata", "Gao_2020.csv"))
Gao_2020_PFHxS_weights <- 1 / (Gao_2020$log_PFHxS_sd^2)
Gao_2020_PFOS_weights <- 1 / (Gao_2020$log_PFOS_sd^2)
Gao_2020_PFBA_weights <- 1 / (Gao_2020$log_PFBA_sd^2)
# PFHxS
Gao_2020_lm_model_PFHxS <- lm(log_PFHxS_mean ~ TL_mean,
data = Gao_2020,
weights = Gao_2020_PFHxS_weights)
Gao_2020_PFHxS_slope <- coef(Gao_2020_lm_model_PFHxS)["TL_mean"]
summary(Gao_2020_lm_model_PFHxS)##
## Call:
## lm(formula = log_PFHxS_mean ~ TL_mean, data = Gao_2020, weights = Gao_2020_PFHxS_weights)
##
## Weighted Residuals:
## 1 2 3 4 5
## -0.1294 0.4357 0.5405 -0.9831 0.8118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.36387 0.20545 6.638 0.00696 **
## TL_mean 0.32083 0.07768 4.130 0.02575 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8415 on 3 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.8504, Adjusted R-squared: 0.8006
## F-statistic: 17.06 on 1 and 3 DF, p-value: 0.02575ggplot(Gao_2020, aes(x = TL_mean, y = log_PFHxS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFHxS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Gao_2020_PFHxS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Gao_2020_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Gao_2020,
weights = Gao_2020_PFOS_weights)
Gao_2020_PFOS_slope <- coef(Gao_2020_lm_model_PFOS)["TL_mean"]
summary(Gao_2020_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Gao_2020, weights = Gao_2020_PFOS_weights)
##
## Weighted Residuals:
## 6 7 8 9 10
## -0.6350 1.8519 0.9278 0.1464 -0.1534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.01656 0.20394 4.985 0.01552 *
## TL_mean 0.53595 0.06377 8.404 0.00353 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.257 on 3 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.9593, Adjusted R-squared: 0.9457
## F-statistic: 70.63 on 1 and 3 DF, p-value: 0.003534ggplot(Gao_2020, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Gao_2020_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFBA
Gao_2020_lm_model_PFBA <- lm(log_PFBA_mean ~ TL_mean,
data = Gao_2020,
weights = Gao_2020_PFBA_weights)
Gao_2020_PFBA_slope <- coef(Gao_2020_lm_model_PFBA)["TL_mean"]
summary(Gao_2020_lm_model_PFBA)##
## Call:
## lm(formula = log_PFBA_mean ~ TL_mean, data = Gao_2020, weights = Gao_2020_PFBA_weights)
##
## Weighted Residuals:
## 11 12 13 14 15
## -2.1590 1.0999 1.9516 -1.4293 -0.8947
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1887 0.7679 2.850 0.0651 .
## TL_mean 0.2200 0.2866 0.768 0.4986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.043 on 3 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.1642, Adjusted R-squared: -0.1145
## F-statistic: 0.5892 on 1 and 3 DF, p-value: 0.4986ggplot(Gao_2020, aes(x = TL_mean, y = log_PFBA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFBA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Gao_2020_PFBA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_021: Chen_2018
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Chen_2018 <- read.csv(here("Rdata", "Chen_2018.csv"))
# PFOS
Chen_2018_lm_model_PFOS <- lm(ln_PFOS_mean ~ TL_mean,
data = Chen_2018)
Chen_2018_PFOS_slope <- coef(Chen_2018_lm_model_PFOS)["TL_mean"]
summary(Chen_2018_lm_model_PFOS)##
## Call:
## lm(formula = ln_PFOS_mean ~ TL_mean, data = Chen_2018)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4006 -0.7029 0.1873 0.6795 0.9389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7077 0.8970 -0.789 0.440984
## TL_mean 1.0804 0.2650 4.076 0.000786 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7952 on 17 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.4943, Adjusted R-squared: 0.4645
## F-statistic: 16.62 on 1 and 17 DF, p-value: 0.0007859ggplot(Chen_2018, aes(x = TL_mean, y = ln_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Chen_2018_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDA
Chen_2018_lm_model_PFDA <- lm(ln_PFDA_mean ~ TL_mean,
data = Chen_2018)
Chen_2018_PFDA_slope <- coef(Chen_2018_lm_model_PFDA)["TL_mean"]
summary(Chen_2018_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ TL_mean, data = Chen_2018)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4924 -0.2783 0.1079 0.3299 1.1225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1265 0.7670 -2.772 0.01304 *
## TL_mean 0.8427 0.2262 3.725 0.00168 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6739 on 17 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.4495, Adjusted R-squared: 0.4171
## F-statistic: 13.88 on 1 and 17 DF, p-value: 0.001682ggplot(Chen_2018, aes(x = TL_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Chen_2018_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Chen_2018_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ TL_mean,
data = Chen_2018)
Chen_2018_PFUnDA_slope <- coef(Chen_2018_lm_model_PFUnDA)["TL_mean"]
summary(Chen_2018_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ TL_mean, data = Chen_2018)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.21192 -0.44358 -0.00398 0.31540 1.26934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.3659 0.7053 -4.772 0.000177 ***
## TL_mean 1.1860 0.2079 5.706 2.57e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6272 on 17 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.657, Adjusted R-squared: 0.6368
## F-statistic: 32.56 on 1 and 17 DF, p-value: 2.574e-05ggplot(Chen_2018, aes(x = TL_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Chen_2018_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Chen_2018_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ TL_mean,
data = Chen_2018)
Chen_2018_PFDoDA_slope <- coef(Chen_2018_lm_model_PFDoDA)["TL_mean"]
summary(Chen_2018_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ TL_mean, data = Chen_2018)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.45482 -0.44963 -0.07221 0.62496 1.86498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.9387 1.0054 -3.917 0.001228 **
## TL_mean 1.3673 0.2943 4.646 0.000269 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8767 on 16 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.5743, Adjusted R-squared: 0.5477
## F-statistic: 21.59 on 1 and 16 DF, p-value: 0.0002691ggplot(Chen_2018, aes(x = TL_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Chen_2018_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_023: Du_2021
- .
Du_2021 <- read.csv(here("Rdata", "Du_2021.csv"))
# PFBA
Du_2021_lm_model_PFBA <- lm(log_PFBA_mean ~ TL_mean,
data = Du_2021)
Du_2021_PFBA_slope <- coef(Du_2021_lm_model_PFBA)["TL_mean"]
summary(Du_2021_lm_model_PFBA)##
## Call:
## lm(formula = log_PFBA_mean ~ TL_mean, data = Du_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.30627 -0.34398 -0.09988 0.38608 1.02936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2951 0.9659 2.376 0.0323 *
## TL_mean -0.6853 0.3139 -2.183 0.0466 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6625 on 14 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.2539, Adjusted R-squared: 0.2006
## F-statistic: 4.765 on 1 and 14 DF, p-value: 0.04657ggplot(Du_2021, aes(x = TL_mean, y = log_PFBA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFBA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Du_2021_PFBA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFHxA
Du_2021_lm_model_PFHxA <- lm(log_PFHxA_mean ~ TL_mean,
data = Du_2021)
Du_2021_PFHxA_slope <- coef(Du_2021_lm_model_PFHxA)["TL_mean"]
summary(Du_2021_lm_model_PFHxA)##
## Call:
## lm(formula = log_PFHxA_mean ~ TL_mean, data = Du_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9300 -0.9866 0.1257 0.5217 3.5225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.709 2.268 4.723 0.000327 ***
## TL_mean -3.637 0.732 -4.968 0.000206 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.532 on 14 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.6381, Adjusted R-squared: 0.6122
## F-statistic: 24.68 on 1 and 14 DF, p-value: 0.0002063ggplot(Du_2021, aes(x = TL_mean, y = log_PFHxA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFHxA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Du_2021_PFHxA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOA
Du_2021_lm_model_PFOA <- lm(log_PFOA_mean ~ TL_mean,
data = Du_2021)
Du_2021_PFOA_slope <- coef(Du_2021_lm_model_PFOA)["TL_mean"]
summary(Du_2021_lm_model_PFOA)##
## Call:
## lm(formula = log_PFOA_mean ~ TL_mean, data = Du_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.268 -1.501 -1.321 2.185 4.022
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.045 3.559 0.575 0.575
## TL_mean -1.538 1.155 -1.331 0.204
##
## Residual standard error: 2.442 on 14 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.1123, Adjusted R-squared: 0.04892
## F-statistic: 1.772 on 1 and 14 DF, p-value: 0.2045ggplot(Du_2021, aes(x = TL_mean, y = log_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Du_2021_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Du_2021_lm_model_PFUnDA <- lm(log_PFUnDA_mean ~ TL_mean,
data = Du_2021)
Du_2021_PFUnDA_slope <- coef(Du_2021_lm_model_PFUnDA)["TL_mean"]
summary(Du_2021_lm_model_PFUnDA)##
## Call:
## lm(formula = log_PFUnDA_mean ~ TL_mean, data = Du_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.93743 -0.77716 0.00012 1.13952 1.96534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2324 2.3010 -0.101 0.921
## TL_mean -0.2403 0.7451 -0.323 0.752
##
## Residual standard error: 1.565 on 14 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.007375, Adjusted R-squared: -0.06353
## F-statistic: 0.104 on 1 and 14 DF, p-value: 0.7518ggplot(Du_2021, aes(x = TL_mean, y = log_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Du_2021_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Du_2021_lm_model_PFTrDA <- lm(log_PFTrDA_mean ~ TL_mean,
data = Du_2021)
Du_2021_PFTrDA_slope <- coef(Du_2021_lm_model_PFTrDA)["TL_mean"]
summary(Du_2021_lm_model_PFTrDA)##
## Call:
## lm(formula = log_PFTrDA_mean ~ TL_mean, data = Du_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1058 -0.8821 -0.3883 1.2197 1.7205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2763 1.8098 0.153 0.881
## TL_mean -0.2579 0.5841 -0.441 0.666
##
## Residual standard error: 1.242 on 14 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.01373, Adjusted R-squared: -0.05672
## F-statistic: 0.1949 on 1 and 14 DF, p-value: 0.6656ggplot(Du_2021, aes(x = TL_mean, y = log_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Du_2021_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFBS
Du_2021_lm_model_PFBS <- lm(log_PFBS_mean ~ TL_mean,
data = Du_2021)
Du_2021_PFBS_slope <- coef(Du_2021_lm_model_PFBS)["TL_mean"]
summary(Du_2021_lm_model_PFBS)##
## Call:
## lm(formula = log_PFBS_mean ~ TL_mean, data = Du_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7926 -0.8373 -0.2716 0.6350 3.2100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2848 2.1601 0.132 0.897
## TL_mean -0.8639 0.7010 -1.232 0.238
##
## Residual standard error: 1.468 on 14 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.09787, Adjusted R-squared: 0.03343
## F-statistic: 1.519 on 1 and 14 DF, p-value: 0.2381ggplot(Du_2021, aes(x = TL_mean, y = log_PFBS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFBS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Du_2021_PFBS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Du_2021_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Du_2021)
Du_2021_PFOS_slope <- coef(Du_2021_lm_model_PFOS)["TL_mean"]
summary(Du_2021_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Du_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9969 -0.8838 0.2544 1.1480 1.5738
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7134 1.7562 -0.406 0.691
## TL_mean 0.4788 0.5691 0.841 0.414
##
## Residual standard error: 1.205 on 14 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.04814, Adjusted R-squared: -0.01986
## F-statistic: 0.708 on 1 and 14 DF, p-value: 0.4143ggplot(Du_2021, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Du_2021_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_035: Ren_2022
- It provides standard errors of individual observations. Weights were assigned (inverse of standard error).
Ren_2022 <- read.csv(here("Rdata", "Ren_2022.csv"))
Ren_2022_weights <- 1 / Ren_2022$se^2
# PFHxS
Ren_2022_lm_model_PFHxS <- lm(log_PFHxS_mean ~ TL_mean,
data = Ren_2022,
weights = Ren_2022_weights)
Ren_2022_PFHxS_slope <- coef(Ren_2022_lm_model_PFHxS)["TL_mean"]
summary(Ren_2022_lm_model_PFHxS)##
## Call:
## lm(formula = log_PFHxS_mean ~ TL_mean, data = Ren_2022, weights = Ren_2022_weights)
##
## Weighted Residuals:
## 15 16 17 18 19 20 21
## -24.37472 85.50317 -18.69589 -59.36783 -55.51457 2.65450 -0.00396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4930 0.7840 -1.904 0.115
## TL_mean 0.2696 0.2946 0.915 0.402
##
## Residual standard error: 54.53 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.1435, Adjusted R-squared: -0.02782
## F-statistic: 0.8376 on 1 and 5 DF, p-value: 0.4021ggplot(Ren_2022, aes(x = TL_mean, y = log_PFHxS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFHxS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFHxS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS - we cannot assign weights because SE = 0
Ren_2022_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFOS_slope <- coef(Ren_2022_lm_model_PFOS)["TL_mean"]
summary(Ren_2022_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 36 37 38 39 40 41 42
## 0.03965 -0.46090 0.14973 0.25790 0.07363 0.02297 -0.08300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0377 0.4638 -2.237 0.07547 .
## TL_mean 0.5930 0.1419 4.179 0.00866 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2513 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.7774, Adjusted R-squared: 0.7329
## F-statistic: 17.46 on 1 and 5 DF, p-value: 0.008664ggplot(Ren_2022, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOA
Ren_2022_lm_model_PFOA <- lm(log_PFOA_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFOA_slope <- coef(Ren_2022_lm_model_PFOA)["TL_mean"]
summary(Ren_2022_lm_model_PFOA)##
## Call:
## lm(formula = log_PFOA_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 29 30 31 32 33 34 35
## -0.05748 0.33727 -0.23359 -0.72102 0.25283 0.69789 -0.27591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.4895 0.9569 -3.647 0.0148 *
## TL_mean 1.0531 0.2919 3.608 0.0154 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5135 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.7225, Adjusted R-squared: 0.667
## F-statistic: 13.02 on 1 and 5 DF, p-value: 0.01541ggplot(Ren_2022, aes(x = TL_mean, y = log_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFNA
Ren_2022_lm_model_PFNA <- lm(log_PFNA_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFNA_slope <- coef(Ren_2022_lm_model_PFNA)["TL_mean"]
summary(Ren_2022_lm_model_PFNA)##
## Call:
## lm(formula = log_PFNA_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 22 23 24 25 26 27 28
## -0.0530391 -0.0315376 -0.0009183 -0.1626527 0.0680890 0.3972287 -0.2171700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4709 0.4058 -3.624 0.01515 *
## TL_mean 0.5376 0.1239 4.340 0.00743 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.219 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.7902, Adjusted R-squared: 0.7483
## F-statistic: 18.84 on 1 and 5 DF, p-value: 0.007429ggplot(Ren_2022, aes(x = TL_mean, y = log_PFNA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFNA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFNA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDA
Ren_2022_lm_model_PFDA <- lm(log_PFDA_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFDA_slope <- coef(Ren_2022_lm_model_PFDA)["TL_mean"]
summary(Ren_2022_lm_model_PFDA)##
## Call:
## lm(formula = log_PFDA_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 1 2 3 4 5 6 7
## -0.315733 -0.048245 0.221757 0.192724 0.312178 0.001054 -0.363735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.3854 0.5370 -6.305 0.00148 **
## TL_mean 0.9916 0.1637 6.057 0.00177 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2892 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.8801, Adjusted R-squared: 0.8561
## F-statistic: 36.69 on 1 and 5 DF, p-value: 0.00177ggplot(Ren_2022, aes(x = TL_mean, y = log_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Ren_2022_lm_model_PFUnDA <- lm(log_PFUnDA_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFUnDA_slope <- coef(Ren_2022_lm_model_PFUnDA)["TL_mean"]
summary(Ren_2022_lm_model_PFUnDA)##
## Call:
## lm(formula = log_PFUnDA_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 57 58 59 60 61 62 63
## -0.063059 0.049001 0.048048 -0.056608 0.194867 -0.162308 -0.009941
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.72208 0.22768 -7.564 0.000641 ***
## TL_mean 0.58765 0.06948 8.458 0.000379 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1235 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.9347, Adjusted R-squared: 0.9216
## F-statistic: 71.54 on 1 and 5 DF, p-value: 0.0003793ggplot(Ren_2022, aes(x = TL_mean, y = log_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Ren_2022_lm_model_PFDoDA <- lm(log_PFDoDA_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFDoDA_slope <- coef(Ren_2022_lm_model_PFDoDA)["TL_mean"]
summary(Ren_2022_lm_model_PFDoDA)##
## Call:
## lm(formula = log_PFDoDA_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 8 9 10 11 12 13 14
## 0.11338 -0.53390 0.14094 0.09166 0.29500 -0.09679 -0.01030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.4146 0.5360 -4.504 0.00637 **
## TL_mean 0.5876 0.1640 3.583 0.01582 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2907 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.7197, Adjusted R-squared: 0.6637
## F-statistic: 12.84 on 1 and 5 DF, p-value: 0.01582ggplot(Ren_2022, aes(x = TL_mean, y = log_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Ren_2022_lm_model_PFTrDA <- lm(log_PFTrDA_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFTrDA_slope <- coef(Ren_2022_lm_model_PFTrDA)["TL_mean"]
summary(Ren_2022_lm_model_PFTrDA)##
## Call:
## lm(formula = log_PFTrDA_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 50 51 52 53 54 55 56
## -0.01362 -0.47249 0.20482 0.21696 0.24087 -0.05042 -0.12611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1104 0.5140 -4.106 0.0093 **
## TL_mean 0.6213 0.1571 3.956 0.0108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2789 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.7579, Adjusted R-squared: 0.7095
## F-statistic: 15.65 on 1 and 5 DF, p-value: 0.01078ggplot(Ren_2022, aes(x = TL_mean, y = log_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTeDA
Ren_2022_lm_model_PFTeDA <- lm(log_PFTeDA_mean ~ TL_mean,
data = Ren_2022)
Ren_2022_PFTeDA_slope <- coef(Ren_2022_lm_model_PFTeDA)["TL_mean"]
summary(Ren_2022_lm_model_PFTeDA)##
## Call:
## lm(formula = log_PFTeDA_mean ~ TL_mean, data = Ren_2022)
##
## Residuals:
## 43 44 45 46 47 48 49
## -0.010601 -0.207935 -0.058366 0.175704 0.192626 0.007405 -0.098833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.25349 0.29090 -7.746 0.000573 ***
## TL_mean 0.44250 0.08888 4.978 0.004181 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1578 on 5 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.8321, Adjusted R-squared: 0.7986
## F-statistic: 24.79 on 1 and 5 DF, p-value: 0.004181ggplot(Ren_2022, aes(x = TL_mean, y = log_PFTeDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFTeDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Ren_2022_PFTeDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_054: Koch_2021
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Food web 1 - freshwater
Koch_2021_freshwater <- read.csv(here("Rdata", "Koch_2021_freshwater.csv"))
Koch_2021_freshwater_lm_model_PFOS <- lm(log_PFOS_mean ~ delta_15N_mean,
data = Koch_2021_freshwater)
Koch_2021_freshwater_PFOS_slope <- coef(Koch_2021_freshwater_lm_model_PFOS)["delta_15N_mean"]
summary(Koch_2021_freshwater_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ delta_15N_mean, data = Koch_2021_freshwater)
##
## Residuals:
## 1 2 3 4 5 6
## -0.06970 0.12586 -0.08788 0.13240 -0.06527 -0.03541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.00747 0.42991 2.343 0.0791 .
## delta_15N_mean 0.09884 0.04235 2.334 0.0799 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1134 on 4 degrees of freedom
## Multiple R-squared: 0.5766, Adjusted R-squared: 0.4707
## F-statistic: 5.447 on 1 and 4 DF, p-value: 0.07992ggplot(Koch_2021_freshwater, aes(x = delta_15N_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Koch_2021_freshwater_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Food web 2 - terrestrial
Koch_2021_terrestrial <- read.csv(here("Rdata", "Koch_2021_terrestrial.csv"))
Koch_2021_terrestrial_lm_model_PFOS <- lm(log_PFOS_mean ~ delta_15N_mean,
data = Koch_2021_terrestrial)
Koch_2021_terrestrial_PFOS_slope <- coef(Koch_2021_terrestrial_lm_model_PFOS)["delta_15N_mean"]
summary(Koch_2021_terrestrial_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ delta_15N_mean, data = Koch_2021_terrestrial)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.79525 -0.08715 0.13252 0.19273 0.41636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26920 0.19164 11.841 3.31e-07 ***
## delta_15N_mean 0.05135 0.04569 1.124 0.287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3553 on 10 degrees of freedom
## Multiple R-squared: 0.1121, Adjusted R-squared: 0.02332
## F-statistic: 1.263 on 1 and 10 DF, p-value: 0.2874ggplot(Koch_2021_terrestrial, aes(x = delta_15N_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Koch_2021_terrestrial_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_056: Li_2008
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Food web 1 - Tilapia excluded
Li_2008_fw1 <- read.csv(here("Rdata", "Li_2008_fw1.csv"))
Li_2008_fw1_lm_model_PFOS <- lm(ln_PFOS_mean ~ TL_mean,
data = Li_2008_fw1)
Li_2008_fw1_PFOS_slope <- coef(Li_2008_fw1_lm_model_PFOS)["TL_mean"]
summary(Li_2008_fw1_lm_model_PFOS)##
## Call:
## lm(formula = ln_PFOS_mean ~ TL_mean, data = Li_2008_fw1)
##
## Residuals:
## 1 2 3 4 5
## -0.08384 0.04462 -0.52825 0.67704 -0.10957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.3121 1.3038 -1.773 0.1743
## TL_mean 1.9157 0.4859 3.943 0.0291 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5028 on 3 degrees of freedom
## Multiple R-squared: 0.8382, Adjusted R-squared: 0.7843
## F-statistic: 15.54 on 1 and 3 DF, p-value: 0.02909ggplot(Li_2008_fw1, aes(x = TL_mean, y = ln_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2008_fw1_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Food web 2 - Tilapia included
Li_2008_fw2 <- read.csv(here("Rdata", "Li_2008_fw2.csv"))
Li_2008_fw2_lm_model_PFOS <- lm(ln_PFOS_mean ~ TL_mean,
data = Li_2008_fw2)
Li_2008_fw2_PFOS_slope <- coef(Li_2008_fw2_lm_model_PFOS)["TL_mean"]
summary(Li_2008_fw2_lm_model_PFOS)##
## Call:
## lm(formula = ln_PFOS_mean ~ TL_mean, data = Li_2008_fw2)
##
## Residuals:
## 1 2 3 4 5 6
## -0.8217 0.1095 -0.3514 0.9595 1.3742 -1.2702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.504 2.131 0.706 0.519
## TL_mean 0.376 0.727 0.517 0.632
##
## Residual standard error: 1.144 on 4 degrees of freedom
## Multiple R-squared: 0.06267, Adjusted R-squared: -0.1717
## F-statistic: 0.2675 on 1 and 4 DF, p-value: 0.6323ggplot(Li_2008_fw2, aes(x = TL_mean, y = ln_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2008_fw2_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_057: Pan_2010
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Pan_2010 <- read.csv(here("Rdata", "Pan_2010.csv"))
Pan_2010_lm_model_PFOS <- lm(log_PFCs_mean ~ TL_mean,
data = Pan_2010)
Pan_2010_PFOS_slope <- coef(Pan_2010_lm_model_PFOS)["TL_mean"]
summary(Pan_2010_lm_model_PFOS)##
## Call:
## lm(formula = log_PFCs_mean ~ TL_mean, data = Pan_2010)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.55463 -0.22439 -0.08986 0.37965 0.54206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3864 0.8956 1.548 0.153
## TL_mean -0.1822 0.3656 -0.498 0.629
##
## Residual standard error: 0.3952 on 10 degrees of freedom
## Multiple R-squared: 0.02424, Adjusted R-squared: -0.07333
## F-statistic: 0.2485 on 1 and 10 DF, p-value: 0.6289ggplot(Pan_2010, aes(x = TL_mean, y = log_PFCs_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Pan_2010_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_058: Van de Vijver_2003
- .
Van_de_Vijver_2003 <- read.csv(here("Rdata", "Van_de_Vijver_2003.csv"))
Van_de_Vijver_2003_lm_model_PFOS <- lm(log_PFOS_mean ~ delta_15N_mean,
data = Van_de_Vijver_2003)
Van_de_Vijver_2003_PFOS_slope <- coef(Van_de_Vijver_2003_lm_model_PFOS)["delta_15N_mean"]
summary(Van_de_Vijver_2003_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ delta_15N_mean, data = Van_de_Vijver_2003)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86597 -0.23342 0.00599 0.21398 0.73505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.26775 0.32963 0.812 0.419
## delta_15N_mean 0.09392 0.01877 5.005 3.82e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.353 on 72 degrees of freedom
## Multiple R-squared: 0.2581, Adjusted R-squared: 0.2478
## F-statistic: 25.05 on 1 and 72 DF, p-value: 3.82e-06ggplot(Van_de_Vijver_2003, aes(x = delta_15N_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Van_de_Vijver_2003_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_061: Barghi_2018
- .
Barghi_2018 <- read.csv(here("Rdata", "Barghi_2018.csv"))
Barghi_2018_lm_model_PFNA <- lm(log_PFNA_mean ~ delta_15N_mean,
data = Barghi_2018)
Barghi_2018_PFNA_slope <- coef(Barghi_2018_lm_model_PFNA)["delta_15N_mean"]
summary(Barghi_2018_lm_model_PFNA)##
## Call:
## lm(formula = log_PFNA_mean ~ delta_15N_mean, data = Barghi_2018)
##
## Residuals:
## 1 2 3 4 5 6
## -0.190716 0.079714 0.068607 0.003892 0.159700 -0.121198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.69815 0.23389 -2.985 0.0405 *
## delta_15N_mean 0.08922 0.02602 3.428 0.0266 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.148 on 4 degrees of freedom
## Multiple R-squared: 0.7461, Adjusted R-squared: 0.6826
## F-statistic: 11.75 on 1 and 4 DF, p-value: 0.02658ggplot(Barghi_2018, aes(x = delta_15N_mean, y = log_PFNA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Barghi_2018_PFNA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_060: Kobayashi_2018
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Kobayashi_2018 <- read.csv(here("Rdata", "Kobayashi_2018.csv"))
# PFHxS
Kobayashi_2018_lm_model_PFHxS <- lm(log_PFHxS_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFHxS_slope <- coef(Kobayashi_2018_lm_model_PFHxS)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFHxS)##
## Call:
## lm(formula = log_PFHxS_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 1 2 3 4 5
## -0.2604 -0.4964 0.0557 0.3224 0.3787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7114 0.8370 -0.850 0.458
## TL_mean 0.5243 0.3206 1.636 0.200
##
## Residual standard error: 0.4339 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.4714, Adjusted R-squared: 0.2952
## F-statistic: 2.675 on 1 and 3 DF, p-value: 0.2004ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFHxS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFHxS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFHxS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Kobayashi_2018_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFOS_slope <- coef(Kobayashi_2018_lm_model_PFOS)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 6 7 8 9 10
## -0.2781 1.0468 0.3711 0.5048 -1.6446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.5615 2.3016 -0.678 0.546
## TL_mean 0.5256 0.8815 0.596 0.593
##
## Residual standard error: 1.193 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.106, Adjusted R-squared: -0.1921
## F-statistic: 0.3555 on 1 and 3 DF, p-value: 0.593ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFPeA
Kobayashi_2018_lm_model_PFPeA <- lm(log_PFPeA_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFPeA_slope <- coef(Kobayashi_2018_lm_model_PFPeA)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFPeA)##
## Call:
## lm(formula = log_PFPeA_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 11 12 13 14 15
## -0.1814 -0.2662 0.2229 0.1064 0.1183
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5678 0.4709 -1.206 0.314
## TL_mean 0.2644 0.1803 1.466 0.239
##
## Residual standard error: 0.2441 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.4174, Adjusted R-squared: 0.2232
## F-statistic: 2.149 on 1 and 3 DF, p-value: 0.2389ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFPeA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFPeA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFPeA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFHxA
Kobayashi_2018_lm_model_PFHxA <- lm(log_PFHxA_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFHxA_slope <- coef(Kobayashi_2018_lm_model_PFHxA)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFHxA)##
## Call:
## lm(formula = log_PFHxA_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 16 17 18 19 20
## -0.2606 -0.7363 0.4511 -0.0337 0.5796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.9110 1.1947 -0.763 0.501
## TL_mean 0.4971 0.4576 1.086 0.357
##
## Residual standard error: 0.6193 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.2824, Adjusted R-squared: 0.04314
## F-statistic: 1.18 on 1 and 3 DF, p-value: 0.3568ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFHxA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFHxA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFHxA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFHpA
Kobayashi_2018_lm_model_PFHpA <- lm(log_PFHpA_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFHpA_slope <- coef(Kobayashi_2018_lm_model_PFHpA)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFHpA)##
## Call:
## lm(formula = log_PFHpA_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 21 22 23 24 25
## -0.28008 -0.23828 0.42687 0.14543 -0.05394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2279 0.6509 -1.886 0.156
## TL_mean 0.3866 0.2493 1.551 0.219
##
## Residual standard error: 0.3374 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.445, Adjusted R-squared: 0.26
## F-statistic: 2.405 on 1 and 3 DF, p-value: 0.2187ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFHpA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFHpA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFHpA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOA
Kobayashi_2018_lm_model_PFOA <- lm(log_PFOA_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFOA_slope <- coef(Kobayashi_2018_lm_model_PFOA)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFOA)##
## Call:
## lm(formula = log_PFOA_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 26 27 28 29 30
## -0.10200 -0.06113 0.77839 -0.40782 -0.20744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25244 1.01433 0.249 0.820
## TL_mean -0.04298 0.38847 -0.111 0.919
##
## Residual standard error: 0.5258 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.004064, Adjusted R-squared: -0.3279
## F-statistic: 0.01224 on 1 and 3 DF, p-value: 0.9189ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFOA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFOA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFNA
Kobayashi_2018_lm_model_PFNA <- lm(log_PFNA_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFNA_slope <- coef(Kobayashi_2018_lm_model_PFNA)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFNA)##
## Call:
## lm(formula = log_PFNA_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 31 32 33 34 35
## -0.08261 -0.10630 0.09168 0.05956 0.03767
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4743 0.1977 2.399 0.0959 .
## TL_mean -0.2353 0.0757 -3.108 0.0530 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1025 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.763, Adjusted R-squared: 0.684
## F-statistic: 9.66 on 1 and 3 DF, p-value: 0.05296ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFNA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFNA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFNA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDA
Kobayashi_2018_lm_model_PFDA <- lm(log_PFDA_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFDA_slope <- coef(Kobayashi_2018_lm_model_PFDA)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFDA)##
## Call:
## lm(formula = log_PFDA_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 36 37 38 39 40
## -0.04724 0.17787 0.12017 0.04292 -0.29371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.30509 0.41138 -0.742 0.512
## TL_mean -0.04466 0.15755 -0.283 0.795
##
## Residual standard error: 0.2132 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.02609, Adjusted R-squared: -0.2986
## F-statistic: 0.08036 on 1 and 3 DF, p-value: 0.7953ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Kobayashi_2018_lm_model_PFUnDA <- lm(log_PFUnDA_mean ~ TL_mean,
data = Kobayashi_2018)
Kobayashi_2018_PFUnDA_slope <- coef(Kobayashi_2018_lm_model_PFUnDA)["TL_mean"]
summary(Kobayashi_2018_lm_model_PFUnDA)##
## Call:
## lm(formula = log_PFUnDA_mean ~ TL_mean, data = Kobayashi_2018)
##
## Residuals:
## 41 42 43 44 45
## -0.3197 0.2082 0.5561 0.2345 -0.6791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04905 1.09747 0.045 0.967
## TL_mean -0.23956 0.42030 -0.570 0.609
##
## Residual standard error: 0.5689 on 3 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.0977, Adjusted R-squared: -0.2031
## F-statistic: 0.3249 on 1 and 3 DF, p-value: 0.6086ggplot(Kobayashi_2018, aes(x = TL_mean, y = log_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Kobayashi_2018_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_047: Heimstad_2024
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Heimstad_2024 <- read.csv(here("Rdata", "Heimstad_2024.csv"))
# PFOS
Heimstad_2024_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Heimstad_2024)
Heimstad_2024_PFOS_slope <- coef(Heimstad_2024_lm_model_PFOS)["TL_mean"]
summary(Heimstad_2024_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Heimstad_2024)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.74638 -0.33568 -0.03996 0.27578 1.65293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.75958 0.24702 3.075 0.00263 **
## TL_mean 0.22448 0.08579 2.616 0.01007 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5877 on 116 degrees of freedom
## (236 observations deleted due to missingness)
## Multiple R-squared: 0.05573, Adjusted R-squared: 0.04759
## F-statistic: 6.846 on 1 and 116 DF, p-value: 0.01007ggplot(Heimstad_2024, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Heimstad_2024_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Heimstad_2024_lm_model_PFUnDA <- lm(log_PFUnDA_mean ~ TL_mean,
data = Heimstad_2024)
Heimstad_2024_PFUnDA_slope <- coef(Heimstad_2024_lm_model_PFUnDA)["TL_mean"]
summary(Heimstad_2024_lm_model_PFUnDA)##
## Call:
## lm(formula = log_PFUnDA_mean ~ TL_mean, data = Heimstad_2024)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.59916 -0.29507 0.05416 0.30239 2.16661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.76304 0.23977 -3.182 0.00187 **
## TL_mean 0.35549 0.08327 4.269 4.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5704 on 116 degrees of freedom
## (236 observations deleted due to missingness)
## Multiple R-squared: 0.1358, Adjusted R-squared: 0.1283
## F-statistic: 18.22 on 1 and 116 DF, p-value: 4.036e-05ggplot(Heimstad_2024, aes(x = TL_mean, y = log_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Heimstad_2024_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Heimstad_2024_lm_model_PFDoDA <- lm(log_PFDoDA_mean ~ TL_mean,
data = Heimstad_2024)
Heimstad_2024_PFDoDA_slope <- coef(Heimstad_2024_lm_model_PFDoDA)["TL_mean"]
summary(Heimstad_2024_lm_model_PFDoDA)##
## Call:
## lm(formula = log_PFDoDA_mean ~ TL_mean, data = Heimstad_2024)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9187 -0.3303 0.0871 0.4149 1.3207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.53645 0.25043 -2.142 0.0343 *
## TL_mean 0.39330 0.08681 4.531 1.44e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5947 on 116 degrees of freedom
## (236 observations deleted due to missingness)
## Multiple R-squared: 0.1504, Adjusted R-squared: 0.143
## F-statistic: 20.53 on 1 and 116 DF, p-value: 1.436e-05ggplot(Heimstad_2024, aes(x = TL_mean, y = log_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Heimstad_2024_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_024: Li_2021
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Li_2021 <- read.csv(here("Rdata", "Li_2021.csv"))
# HFPO-TeA
Li_2021_lm_model_HFPOTeA <- lm(log_HFPO.TeA_mean ~ TL_mean,
data = Li_2021)
Li_2021_HFPOTeA_slope <- coef(Li_2021_lm_model_HFPOTeA)["TL_mean"]
summary(Li_2021_lm_model_HFPOTeA)##
## Call:
## lm(formula = log_HFPO.TeA_mean ~ TL_mean, data = Li_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.60898 -0.27421 -0.08415 0.30463 0.57852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1650 0.4681 -4.625 0.000944 ***
## TL_mean 0.4675 0.1447 3.230 0.009024 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4054 on 10 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.5106, Adjusted R-squared: 0.4616
## F-statistic: 10.43 on 1 and 10 DF, p-value: 0.009024ggplot(Li_2021, aes(x = TL_mean, y = log_HFPO.TeA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [HFPOTeA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2021_HFPOTeA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# HFPO-TrA
Li_2021_lm_model_HFPOTrA <- lm(log_HFPO.TrA_mean ~ TL_mean,
data = Li_2021)
Li_2021_HFPOTrA_slope <- coef(Li_2021_lm_model_HFPOTrA)["TL_mean"]
summary(Li_2021_lm_model_HFPOTrA)##
## Call:
## lm(formula = log_HFPO.TrA_mean ~ TL_mean, data = Li_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0859 -0.1653 0.0324 0.4348 0.5844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4541 0.6460 0.703 0.498
## TL_mean 0.2841 0.2002 1.419 0.186
##
## Residual standard error: 0.5601 on 10 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.1677, Adjusted R-squared: 0.08444
## F-statistic: 2.014 on 1 and 10 DF, p-value: 0.1862ggplot(Li_2021, aes(x = TL_mean, y = log_HFPO.TrA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [HFPOTrA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2021_HFPOTrA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFO5DoA
Li_2021_lm_model_PFO5DoA <- lm(log_PFO5DoA_mean ~ TL_mean,
data = Li_2021)
Li_2021_PFO5DoA_slope <- coef(Li_2021_lm_model_PFO5DoA)["TL_mean"]
summary(Li_2021_lm_model_PFO5DoA)##
## Call:
## lm(formula = log_PFO5DoA_mean ~ TL_mean, data = Li_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64644 -0.18210 0.05051 0.28434 0.40177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.8048 0.4239 -6.616 5.96e-05 ***
## TL_mean 0.7513 0.1315 5.714 0.000194 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3681 on 10 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.7655, Adjusted R-squared: 0.7421
## F-statistic: 32.65 on 1 and 10 DF, p-value: 0.0001945ggplot(Li_2021, aes(x = TL_mean, y = log_PFO5DoA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFO5DoA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2021_PFO5DoA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# H-PFMO2OSA
Li_2021_lm_model_HPFMO2OSA <- lm(log_H.PFMO2OSA_mean ~ TL_mean,
data = Li_2021)
Li_2021_HPFMO2OSA_slope <- coef(Li_2021_lm_model_HPFMO2OSA)["TL_mean"]
summary(Li_2021_lm_model_HPFMO2OSA)##
## Call:
## lm(formula = log_H.PFMO2OSA_mean ~ TL_mean, data = Li_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.35963 -0.28453 0.00132 0.26375 0.39865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7624 0.3559 -4.951 0.000577 ***
## TL_mean 0.4237 0.1102 3.843 0.003247 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3096 on 10 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.5963, Adjusted R-squared: 0.556
## F-statistic: 14.77 on 1 and 10 DF, p-value: 0.003247ggplot(Li_2021, aes(x = TL_mean, y = log_H.PFMO2OSA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [HPFMO2OSA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2021_HPFMO2OSA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Li_2021_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Li_2021)
Li_2021_PFOS_slope <- coef(Li_2021_lm_model_PFOS)["TL_mean"]
summary(Li_2021_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Li_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3298 -0.1600 0.0072 0.1341 0.4334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07775 0.28087 0.277 0.788
## TL_mean 0.21184 0.08690 2.438 0.035 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2436 on 10 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.3728, Adjusted R-squared: 0.31
## F-statistic: 5.943 on 1 and 10 DF, p-value: 0.03498ggplot(Li_2021, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2021_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFMOAA
Li_2021_lm_model_PFMOAA <- lm(log_PFMOAA_mean ~ TL_mean,
data = Li_2021)
Li_2021_PFMOAA_slope <- coef(Li_2021_lm_model_PFMOAA)["TL_mean"]
summary(Li_2021_lm_model_PFMOAA)##
## Call:
## lm(formula = log_PFMOAA_mean ~ TL_mean, data = Li_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.73024 -0.09999 0.02142 0.11323 1.87431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1747 1.7081 2.444 0.0403 *
## TL_mean -0.5879 0.4960 -1.185 0.2699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.935 on 8 degrees of freedom
## (70 observations deleted due to missingness)
## Multiple R-squared: 0.1494, Adjusted R-squared: 0.04305
## F-statistic: 1.405 on 1 and 8 DF, p-value: 0.2699ggplot(Li_2021, aes(x = TL_mean, y = log_PFMOAA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFMOAA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2021_PFMOAA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFBA
Li_2021_lm_model_PFBA <- lm(log_PFBA_mean ~ TL_mean,
data = Li_2021)
Li_2021_PFBA_slope <- coef(Li_2021_lm_model_PFBA)["TL_mean"]
summary(Li_2021_lm_model_PFBA)##
## Call:
## lm(formula = log_PFBA_mean ~ TL_mean, data = Li_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.91289 -0.30435 -0.03787 0.10775 1.43887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3740 1.1921 1.991 0.0816 .
## TL_mean -0.4423 0.3456 -1.280 0.2365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6476 on 8 degrees of freedom
## (70 observations deleted due to missingness)
## Multiple R-squared: 0.1699, Adjusted R-squared: 0.06615
## F-statistic: 1.638 on 1 and 8 DF, p-value: 0.2365ggplot(Li_2021, aes(x = TL_mean, y = log_PFBA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFBA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Li_2021_PFBA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_025: Pan_2021
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Pan_2021 <- read.csv(here("Rdata", "Pan_2021.csv"))
# 6:2 Cl-PFESA
Pan_2021_lm_model_62ClPFESA <- lm(log_62ClPFESA_mean ~ TL_mean,
data = Pan_2021)
Pan_2021_62ClPFESA_slope <- coef(Pan_2021_lm_model_62ClPFESA)["TL_mean"]
summary(Pan_2021_lm_model_62ClPFESA)##
## Call:
## lm(formula = log_62ClPFESA_mean ~ TL_mean, data = Pan_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20704 -0.14161 -0.02182 0.13676 0.16199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1075 0.1931 -10.916 6.45e-08 ***
## TL_mean 0.2529 0.0630 4.014 0.00147 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1547 on 13 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.5534, Adjusted R-squared: 0.5191
## F-statistic: 16.11 on 1 and 13 DF, p-value: 0.001473ggplot(Pan_2021, aes(x = TL_mean, y = log_62ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [62ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Pan_2021_62ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Pan_2021_lm_model_PFOS <- lm(log_PFOS_mean ~ TL_mean,
data = Pan_2021)
Pan_2021_PFOS_slope <- coef(Pan_2021_lm_model_PFOS)["TL_mean"]
summary(Pan_2021_lm_model_PFOS)##
## Call:
## lm(formula = log_PFOS_mean ~ TL_mean, data = Pan_2021)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45246 -0.24331 0.03005 0.18397 0.63335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0933 0.3408 -3.208 0.00334 **
## TL_mean 0.2452 0.1059 2.315 0.02817 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2946 on 28 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1606, Adjusted R-squared: 0.1307
## F-statistic: 5.359 on 1 and 28 DF, p-value: 0.02817ggplot(Pan_2021, aes(x = TL_mean, y = log_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "Trophic level",
y = "log [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Pan_2021_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
S_059: Jiao_2023
- It does not provide standard error or standard deviation values of individual observations. Weights cannot be assigned.
Jiao_2023_blood <- read.csv(here("Rdata", "Jiao_2023_blood.csv"))
# PFDA
Jiao_2023_blood_lm_model_PFDA <- lm(ln_PFDA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_PFDA_slope <- coef(Jiao_2023_blood_lm_model_PFDA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9485 -0.8834 -0.1819 1.0004 1.9293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.5117 2.7998 -0.540 0.602
## delta_15N_mean 0.3034 0.2488 1.219 0.254
##
## Residual standard error: 1.388 on 9 degrees of freedom
## (105 observations deleted due to missingness)
## Multiple R-squared: 0.1418, Adjusted R-squared: 0.04643
## F-statistic: 1.487 on 1 and 9 DF, p-value: 0.2537ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Jiao_2023_blood_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_PFUnDA_slope <- coef(Jiao_2023_blood_lm_model_PFUnDA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7250 -0.6047 -0.0600 1.2075 2.6755
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.9167 2.6573 -1.474 0.1746
## delta_15N_mean 0.5057 0.2271 2.227 0.0529 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.087 on 9 degrees of freedom
## (105 observations deleted due to missingness)
## Multiple R-squared: 0.3553, Adjusted R-squared: 0.2837
## F-statistic: 4.96 on 1 and 9 DF, p-value: 0.05294ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Jiao_2023_blood_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_PFDoDA_slope <- coef(Jiao_2023_blood_lm_model_PFDoDA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4957 0.2035 0.5562 1.1635 2.7003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.3056 3.4832 -1.236 0.248
## delta_15N_mean 0.3684 0.2873 1.282 0.232
##
## Residual standard error: 2.45 on 9 degrees of freedom
## (105 observations deleted due to missingness)
## Multiple R-squared: 0.1545, Adjusted R-squared: 0.06054
## F-statistic: 1.644 on 1 and 9 DF, p-value: 0.2318ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Jiao_2023_blood_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_PFTrDA_slope <- coef(Jiao_2023_blood_lm_model_PFTrDA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9531 -0.3559 0.5952 1.4231 2.8866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.6603 4.8647 -0.958 0.363
## delta_15N_mean 0.4555 0.3981 1.144 0.282
##
## Residual standard error: 2.761 on 9 degrees of freedom
## (105 observations deleted due to missingness)
## Multiple R-squared: 0.127, Adjusted R-squared: 0.02996
## F-statistic: 1.309 on 1 and 9 DF, p-value: 0.2821ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFOS
Jiao_2023_blood_lm_model_PFOS <- lm(ln_PFOS_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_PFOS_slope <- coef(Jiao_2023_blood_lm_model_PFOS)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_PFOS)##
## Call:
## lm(formula = ln_PFOS_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5588 -0.7950 -0.3385 0.6065 3.1227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3299 1.2159 -0.271 0.79075
## delta_15N_mean 0.3609 0.1088 3.319 0.00613 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 12 degrees of freedom
## (102 observations deleted due to missingness)
## Multiple R-squared: 0.4785, Adjusted R-squared: 0.4351
## F-statistic: 11.01 on 1 and 12 DF, p-value: 0.006127ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_PFOS_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFOS]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_PFOS_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# FOSA
Jiao_2023_blood_lm_model_FOSA <- lm(ln_FOSA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_FOSA_slope <- coef(Jiao_2023_blood_lm_model_FOSA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_FOSA)##
## Call:
## lm(formula = ln_FOSA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0713 -1.2962 -0.0243 0.9772 4.4240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.1248 2.6092 -1.964 0.0779 .
## delta_15N_mean 0.6399 0.2198 2.911 0.0155 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.038 on 10 degrees of freedom
## (104 observations deleted due to missingness)
## Multiple R-squared: 0.4587, Adjusted R-squared: 0.4046
## F-statistic: 8.475 on 1 and 10 DF, p-value: 0.01553ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_FOSA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [FOSA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_FOSA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 62ClPFESA
Jiao_2023_blood_lm_model_62ClPFESA <- lm(ln_62ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_62ClPFESA_slope <- coef(Jiao_2023_blood_lm_model_62ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_62ClPFESA)##
## Call:
## lm(formula = ln_62ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9422 -1.0807 0.6322 0.8136 1.6094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.8421 1.2853 -5.323 0.000336 ***
## delta_15N_mean 0.7490 0.1151 6.506 6.84e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.323 on 10 degrees of freedom
## (104 observations deleted due to missingness)
## Multiple R-squared: 0.8089, Adjusted R-squared: 0.7898
## F-statistic: 42.33 on 1 and 10 DF, p-value: 6.841e-05ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_62ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [6:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_62ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 82ClPFESA
Jiao_2023_blood_lm_model_82ClPFESA <- lm(ln_82ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_82ClPFESA_slope <- coef(Jiao_2023_blood_lm_model_82ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_82ClPFESA)##
## Call:
## lm(formula = ln_82ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2175 -0.9011 -0.3723 1.3603 4.0079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.0330 4.1715 -0.727 0.488
## delta_15N_mean 0.3383 0.3445 0.982 0.355
##
## Residual standard error: 2.348 on 8 degrees of freedom
## (106 observations deleted due to missingness)
## Multiple R-squared: 0.1076, Adjusted R-squared: -0.003972
## F-statistic: 0.9644 on 1 and 8 DF, p-value: 0.3548ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_82ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [8:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_82ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 102ClPFESA
Jiao_2023_blood_lm_model_102ClPFESA <- lm(ln_102ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_102ClPFESA_slope <- coef(Jiao_2023_blood_lm_model_102ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_102ClPFESA)##
## Call:
## lm(formula = ln_102ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3005 -1.5953 -0.7154 1.3045 4.0961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.5705 3.2223 -2.039 0.0688 .
## delta_15N_mean 0.5235 0.2710 1.932 0.0822 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.57 on 10 degrees of freedom
## (104 observations deleted due to missingness)
## Multiple R-squared: 0.2718, Adjusted R-squared: 0.199
## F-statistic: 3.733 on 1 and 10 DF, p-value: 0.08216ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_102ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [10:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_102ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 103FTCA
Jiao_2023_blood_lm_model_103FTCA <- lm(ln_103FTCA_mean ~ delta_15N_mean,
data = Jiao_2023_blood)
Jiao_2023_blood_103FTCA_slope <- coef(Jiao_2023_blood_lm_model_103FTCA)["delta_15N_mean"]
summary(Jiao_2023_blood_lm_model_103FTCA)##
## Call:
## lm(formula = ln_103FTCA_mean ~ delta_15N_mean, data = Jiao_2023_blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.483 -2.327 1.264 2.370 4.668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.5428 4.7226 -1.174 0.268
## delta_15N_mean 0.5273 0.3998 1.319 0.217
##
## Residual standard error: 3.722 on 10 degrees of freedom
## (104 observations deleted due to missingness)
## Multiple R-squared: 0.1482, Adjusted R-squared: 0.06303
## F-statistic: 1.74 on 1 and 10 DF, p-value: 0.2166ggplot(Jiao_2023_blood, aes(x = delta_15N_mean, y = ln_103FTCA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [10:3 FTCA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_blood_103FTCA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Jiao_2023_gill <- read.csv(here("Rdata", "Jiao_2023_gill.csv"))
# PFDA
Jiao_2023_gill_lm_model_PFDA <- lm(ln_PFDA_mean ~ delta_15N_mean,
data = Jiao_2023_gill)
Jiao_2023_gill_PFDA_slope <- coef(Jiao_2023_gill_lm_model_PFDA)["delta_15N_mean"]
summary(Jiao_2023_gill_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ delta_15N_mean, data = Jiao_2023_gill)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9513 -0.5570 -0.1315 0.8475 3.6921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.2607 3.5614 -1.477 0.1779
## delta_15N_mean 0.5783 0.2904 1.991 0.0816 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.032 on 8 degrees of freedom
## (26 observations deleted due to missingness)
## Multiple R-squared: 0.3313, Adjusted R-squared: 0.2478
## F-statistic: 3.964 on 1 and 8 DF, p-value: 0.08164ggplot(Jiao_2023_gill, aes(x = delta_15N_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_gill_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Jiao_2023_gill_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ delta_15N_mean,
data = Jiao_2023_gill)
Jiao_2023_gill_PFDoDA_slope <- coef(Jiao_2023_gill_lm_model_PFDoDA)["delta_15N_mean"]
summary(Jiao_2023_gill_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ delta_15N_mean, data = Jiao_2023_gill)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2125 -0.9288 0.1388 0.8184 0.9794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1670 2.1165 0.551 0.599
## delta_15N_mean 0.0886 0.1697 0.522 0.618
##
## Residual standard error: 0.9274 on 7 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.03747, Adjusted R-squared: -0.1
## F-statistic: 0.2725 on 1 and 7 DF, p-value: 0.6178ggplot(Jiao_2023_gill, aes(x = delta_15N_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_gill_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Jiao_2023_gill_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ delta_15N_mean,
data = Jiao_2023_gill)
Jiao_2023_gill_PFTrDA_slope <- coef(Jiao_2023_gill_lm_model_PFTrDA)["delta_15N_mean"]
summary(Jiao_2023_gill_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ delta_15N_mean, data = Jiao_2023_gill)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0771 -0.1706 0.3035 0.4034 0.4712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8281 1.5708 1.164 0.289
## delta_15N_mean 0.1236 0.1255 0.985 0.362
##
## Residual standard error: 0.6786 on 6 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.1393, Adjusted R-squared: -0.004163
## F-statistic: 0.971 on 1 and 6 DF, p-value: 0.3625ggplot(Jiao_2023_gill, aes(x = delta_15N_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_gill_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 82ClPFESA
Jiao_2023_gill_lm_model_82ClPFESA <- lm(ln_82ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_gill)
Jiao_2023_gill_82ClPFESA_slope <- coef(Jiao_2023_gill_lm_model_82ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_gill_lm_model_82ClPFESA)##
## Call:
## lm(formula = ln_82ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_gill)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.28167 -1.20091 0.07748 1.30268 2.03560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6232 3.7657 0.962 0.368
## delta_15N_mean -0.1355 0.2975 -0.455 0.663
##
## Residual standard error: 1.656 on 7 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.02878, Adjusted R-squared: -0.11
## F-statistic: 0.2074 on 1 and 7 DF, p-value: 0.6626ggplot(Jiao_2023_gill, aes(x = delta_15N_mean, y = ln_82ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [8:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_gill_82ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Jiao_2023_heart <- read.csv(here("Rdata", "Jiao_2023_heart.csv"))
# PFDA
Jiao_2023_heart_lm_model_PFDA <- lm(ln_PFDA_mean ~ delta_15N_mean,
data = Jiao_2023_heart)
Jiao_2023_heart_PFDA_slope <- coef(Jiao_2023_heart_lm_model_PFDA)["delta_15N_mean"]
summary(Jiao_2023_heart_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ delta_15N_mean, data = Jiao_2023_heart)
##
## Residuals:
## 1 2 3 4 5 6 7
## 0.22439 -0.41495 -0.54878 1.07947 0.14027 -0.08531 -0.39508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.33447 1.44227 2.312 0.0687 .
## delta_15N_mean -0.03311 0.11450 -0.289 0.7840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6119 on 5 degrees of freedom
## (37 observations deleted due to missingness)
## Multiple R-squared: 0.01645, Adjusted R-squared: -0.1803
## F-statistic: 0.08364 on 1 and 5 DF, p-value: 0.784ggplot(Jiao_2023_heart, aes(x = delta_15N_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [8:2 PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_heart_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Jiao_2023_heart_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ delta_15N_mean,
data = Jiao_2023_heart)
Jiao_2023_heart_PFUnDA_slope <- coef(Jiao_2023_heart_lm_model_PFUnDA)["delta_15N_mean"]
summary(Jiao_2023_heart_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ delta_15N_mean, data = Jiao_2023_heart)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.77430 -0.25561 0.06962 0.25863 0.92137
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1489 1.2404 2.539 0.0387 *
## delta_15N_mean 0.0336 0.0995 0.338 0.7455
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5347 on 7 degrees of freedom
## (35 observations deleted due to missingness)
## Multiple R-squared: 0.01603, Adjusted R-squared: -0.1245
## F-statistic: 0.1141 on 1 and 7 DF, p-value: 0.7455ggplot(Jiao_2023_heart, aes(x = delta_15N_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [8:2 PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_heart_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Jiao_2023_heart_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ delta_15N_mean,
data = Jiao_2023_heart)
Jiao_2023_heart_PFDoDA_slope <- coef(Jiao_2023_heart_lm_model_PFDoDA)["delta_15N_mean"]
summary(Jiao_2023_heart_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ delta_15N_mean, data = Jiao_2023_heart)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.14021 -0.34351 0.01224 0.53050 0.96394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.15976 1.69637 1.273 0.239
## delta_15N_mean 0.04931 0.13470 0.366 0.724
##
## Residual standard error: 0.7383 on 8 degrees of freedom
## (34 observations deleted due to missingness)
## Multiple R-squared: 0.01648, Adjusted R-squared: -0.1065
## F-statistic: 0.134 on 1 and 8 DF, p-value: 0.7238ggplot(Jiao_2023_heart, aes(x = delta_15N_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_heart_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Jiao_2023_heart_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ delta_15N_mean,
data = Jiao_2023_heart)
Jiao_2023_heart_PFTrDA_slope <- coef(Jiao_2023_heart_lm_model_PFTrDA)["delta_15N_mean"]
summary(Jiao_2023_heart_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ delta_15N_mean, data = Jiao_2023_heart)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8926 -0.4484 0.1651 0.3702 0.6632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3425 1.3482 1.738 0.126
## delta_15N_mean 0.1179 0.1074 1.098 0.309
##
## Residual standard error: 0.5874 on 7 degrees of freedom
## (35 observations deleted due to missingness)
## Multiple R-squared: 0.1469, Adjusted R-squared: 0.02497
## F-statistic: 1.205 on 1 and 7 DF, p-value: 0.3087ggplot(Jiao_2023_heart, aes(x = delta_15N_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_heart_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 62ClPFESA
Jiao_2023_heart_lm_model_62ClPFESA <- lm(ln_62ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_heart)
Jiao_2023_heart_62ClPFESA_slope <- coef(Jiao_2023_heart_lm_model_62ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_heart_lm_model_62ClPFESA)##
## Call:
## lm(formula = ln_62ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_heart)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3347 -0.2279 0.1028 0.7822 1.2291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.6456 1.9421 -1.362 0.2153
## delta_15N_mean 0.4853 0.1586 3.060 0.0183 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.152 on 7 degrees of freedom
## (35 observations deleted due to missingness)
## Multiple R-squared: 0.5722, Adjusted R-squared: 0.511
## F-statistic: 9.362 on 1 and 7 DF, p-value: 0.01833ggplot(Jiao_2023_heart, aes(x = delta_15N_mean, y = ln_62ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [6:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_heart_62ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Jiao_2023_kidney <- read.csv(here("Rdata", "Jiao_2023_kidney.csv"))
# PFUnDA
Jiao_2023_kidney_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ delta_15N_mean,
data = Jiao_2023_kidney)
Jiao_2023_kidney_PFUnDA_slope <- coef(Jiao_2023_kidney_lm_model_PFUnDA)["delta_15N_mean"]
summary(Jiao_2023_kidney_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ delta_15N_mean, data = Jiao_2023_kidney)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6179 -0.4968 0.2868 0.6245 0.9448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.83232 2.17506 1.762 0.121
## delta_15N_mean 0.00028 0.17168 0.002 0.999
##
## Residual standard error: 0.9457 on 7 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 3.799e-07, Adjusted R-squared: -0.1429
## F-statistic: 2.659e-06 on 1 and 7 DF, p-value: 0.9987ggplot(Jiao_2023_kidney, aes(x = delta_15N_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_kidney_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Jiao_2023_kidney_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ delta_15N_mean,
data = Jiao_2023_kidney)
Jiao_2023_kidney_PFDoDA_slope <- coef(Jiao_2023_kidney_lm_model_PFDoDA)["delta_15N_mean"]
summary(Jiao_2023_kidney_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ delta_15N_mean, data = Jiao_2023_kidney)
##
## Residuals:
## 10 11 12 13 14 15 16
## -0.3800 0.7300 0.8183 0.9064 -0.4192 -1.0168 -0.6386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.80465 2.11220 1.801 0.132
## delta_15N_mean -0.06733 0.16667 -0.404 0.703
##
## Residual standard error: 0.8702 on 5 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.0316, Adjusted R-squared: -0.1621
## F-statistic: 0.1632 on 1 and 5 DF, p-value: 0.703ggplot(Jiao_2023_kidney, aes(x = delta_15N_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_kidney_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Jiao_2023_kidney_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ delta_15N_mean,
data = Jiao_2023_kidney)
Jiao_2023_kidney_PFTrDA_slope <- coef(Jiao_2023_kidney_lm_model_PFTrDA)["delta_15N_mean"]
summary(Jiao_2023_kidney_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ delta_15N_mean, data = Jiao_2023_kidney)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5706 -0.2251 0.2534 0.3015 0.8134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.24614 1.73781 1.868 0.104
## delta_15N_mean 0.05048 0.13760 0.367 0.725
##
## Residual standard error: 0.748 on 7 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.01887, Adjusted R-squared: -0.1213
## F-statistic: 0.1346 on 1 and 7 DF, p-value: 0.7245ggplot(Jiao_2023_kidney, aes(x = delta_15N_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_kidney_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Jiao_2023_liver <- read.csv(here("Rdata", "Jiao_2023_liver.csv"))
# PFDA
Jiao_2023_liver_lm_model_PFDA <- lm(ln_PFDA_mean ~ delta_15N_mean,
data = Jiao_2023_liver)
Jiao_2023_liver_PFDA_slope <- coef(Jiao_2023_liver_lm_model_PFDA)["delta_15N_mean"]
summary(Jiao_2023_liver_lm_model_PFDA)##
## Call:
## lm(formula = ln_PFDA_mean ~ delta_15N_mean, data = Jiao_2023_liver)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0891 -1.0651 -0.2310 0.7079 3.2544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.4914 2.2780 -2.850 0.01910 *
## delta_15N_mean 0.7180 0.1876 3.826 0.00405 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.612 on 9 degrees of freedom
## (53 observations deleted due to missingness)
## Multiple R-squared: 0.6193, Adjusted R-squared: 0.577
## F-statistic: 14.64 on 1 and 9 DF, p-value: 0.00405ggplot(Jiao_2023_liver, aes(x = delta_15N_mean, y = ln_PFDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_liver_PFDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFUnDA
Jiao_2023_liver_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ delta_15N_mean,
data = Jiao_2023_liver)
Jiao_2023_liver_PFUnDA_slope <- coef(Jiao_2023_liver_lm_model_PFUnDA)["delta_15N_mean"]
summary(Jiao_2023_liver_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ delta_15N_mean, data = Jiao_2023_liver)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3759 -0.9360 -0.4813 0.7422 3.3797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.4163 2.4220 -2.649 0.02651 *
## delta_15N_mean 0.7508 0.1999 3.756 0.00451 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.71 on 9 degrees of freedom
## (53 observations deleted due to missingness)
## Multiple R-squared: 0.6106, Adjusted R-squared: 0.5673
## F-statistic: 14.11 on 1 and 9 DF, p-value: 0.004509ggplot(Jiao_2023_liver, aes(x = delta_15N_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_liver_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFDoDA
Jiao_2023_liver_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ delta_15N_mean,
data = Jiao_2023_liver)
Jiao_2023_liver_PFDoDA_slope <- coef(Jiao_2023_liver_lm_model_PFDoDA)["delta_15N_mean"]
summary(Jiao_2023_liver_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ delta_15N_mean, data = Jiao_2023_liver)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9904 -0.6670 -0.2602 0.4470 2.9158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.5156 2.0654 -3.155 0.01350 *
## delta_15N_mean 0.6938 0.1704 4.071 0.00358 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.449 on 8 degrees of freedom
## (54 observations deleted due to missingness)
## Multiple R-squared: 0.6744, Adjusted R-squared: 0.6337
## F-statistic: 16.57 on 1 and 8 DF, p-value: 0.003578ggplot(Jiao_2023_liver, aes(x = delta_15N_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_liver_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Jiao_2023_liver_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ delta_15N_mean,
data = Jiao_2023_liver)
Jiao_2023_liver_PFTrDA_slope <- coef(Jiao_2023_liver_lm_model_PFTrDA)["delta_15N_mean"]
summary(Jiao_2023_liver_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ delta_15N_mean, data = Jiao_2023_liver)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1526 -0.6326 -0.1307 0.3852 3.0592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.0093 2.0954 -3.345 0.01015 *
## delta_15N_mean 0.8100 0.1749 4.631 0.00169 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.463 on 8 degrees of freedom
## (54 observations deleted due to missingness)
## Multiple R-squared: 0.7283, Adjusted R-squared: 0.6944
## F-statistic: 21.45 on 1 and 8 DF, p-value: 0.001686ggplot(Jiao_2023_liver, aes(x = delta_15N_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_liver_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 62ClPFESA
Jiao_2023_liver_lm_model_62ClPFESA <- lm(ln_62ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_liver)
Jiao_2023_liver_62ClPFESA_slope <- coef(Jiao_2023_liver_lm_model_62ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_liver_lm_model_62ClPFESA)##
## Call:
## lm(formula = ln_62ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_liver)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0695 -0.4632 0.1257 0.2567 1.8627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03825 1.59778 0.024 0.9814
## delta_15N_mean 0.34264 0.13189 2.598 0.0288 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.136 on 9 degrees of freedom
## (53 observations deleted due to missingness)
## Multiple R-squared: 0.4285, Adjusted R-squared: 0.365
## F-statistic: 6.749 on 1 and 9 DF, p-value: 0.02884ggplot(Jiao_2023_liver, aes(x = delta_15N_mean, y = ln_62ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [6:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_liver_62ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 82ClPFESA
Jiao_2023_liver_lm_model_82ClPFESA <- lm(ln_82ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_liver)
Jiao_2023_liver_82ClPFESA_slope <- coef(Jiao_2023_liver_lm_model_82ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_liver_lm_model_82ClPFESA)##
## Call:
## lm(formula = ln_82ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_liver)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3520 -1.3920 -0.3272 1.1658 3.7963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.0501 2.8319 -2.136 0.0614 .
## delta_15N_mean 0.6803 0.2333 2.916 0.0171 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.01 on 9 degrees of freedom
## (53 observations deleted due to missingness)
## Multiple R-squared: 0.4858, Adjusted R-squared: 0.4287
## F-statistic: 8.504 on 1 and 9 DF, p-value: 0.01714ggplot(Jiao_2023_liver, aes(x = delta_15N_mean, y = ln_82ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [8:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_liver_82ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Jiao_2023_muscle <- read.csv(here("Rdata", "Jiao_2023_muscle.csv"))
# PFDoDA
Jiao_2023_muscle_lm_model_PFDoDA <- lm(ln_PFDoDA_mean ~ delta_15N_mean,
data = Jiao_2023_muscle)
Jiao_2023_muscle_PFDoDA_slope <- coef(Jiao_2023_muscle_lm_model_PFDoDA)["delta_15N_mean"]
summary(Jiao_2023_muscle_lm_model_PFDoDA)##
## Call:
## lm(formula = ln_PFDoDA_mean ~ delta_15N_mean, data = Jiao_2023_muscle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.03159 -0.76392 -0.01856 0.56099 1.60137
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.95680 2.14355 0.913 0.392
## delta_15N_mean -0.06564 0.16930 -0.388 0.710
##
## Residual standard error: 0.9253 on 7 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 0.02102, Adjusted R-squared: -0.1188
## F-statistic: 0.1503 on 1 and 7 DF, p-value: 0.7098ggplot(Jiao_2023_muscle, aes(x = delta_15N_mean, y = ln_PFDoDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFDoDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_muscle_PFDoDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Jiao_2023_muscle_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ delta_15N_mean,
data = Jiao_2023_muscle)
Jiao_2023_muscle_PFTrDA_slope <- coef(Jiao_2023_muscle_lm_model_PFTrDA)["delta_15N_mean"]
summary(Jiao_2023_muscle_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ delta_15N_mean, data = Jiao_2023_muscle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.94831 -0.74944 -0.02414 0.39733 2.02594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.3150 1.6215 -0.811 0.4363
## delta_15N_mean 0.2806 0.1314 2.135 0.0585 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.93 on 10 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.3132, Adjusted R-squared: 0.2445
## F-statistic: 4.56 on 1 and 10 DF, p-value: 0.05849ggplot(Jiao_2023_muscle, aes(x = delta_15N_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_muscle_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 62ClPFESA
Jiao_2023_muscle_lm_model_62ClPFESA <- lm(ln_62ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_muscle)
Jiao_2023_muscle_62ClPFESA_slope <- coef(Jiao_2023_muscle_lm_model_62ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_muscle_lm_model_62ClPFESA)##
## Call:
## lm(formula = ln_62ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_muscle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.27143 -0.75178 -0.06873 0.90211 1.44098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.8798 1.2751 -1.474 0.1712
## delta_15N_mean 0.2668 0.1078 2.476 0.0328 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.002 on 10 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.38, Adjusted R-squared: 0.318
## F-statistic: 6.129 on 1 and 10 DF, p-value: 0.03278ggplot(Jiao_2023_muscle, aes(x = delta_15N_mean, y = ln_62ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [6:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_muscle_62ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
Jiao_2023_pancreas <- read.csv(here("Rdata", "Jiao_2023_pancreas.csv"))
# PFUnDA
Jiao_2023_pancreas_lm_model_PFUnDA <- lm(ln_PFUnDA_mean ~ delta_15N_mean,
data = Jiao_2023_pancreas)
Jiao_2023_pancreas_PFUnDA_slope <- coef(Jiao_2023_pancreas_lm_model_PFUnDA)["delta_15N_mean"]
summary(Jiao_2023_pancreas_lm_model_PFUnDA)##
## Call:
## lm(formula = ln_PFUnDA_mean ~ delta_15N_mean, data = Jiao_2023_pancreas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3888 -0.7156 0.2062 0.6493 1.1620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0299 2.2383 0.460 0.662
## delta_15N_mean 0.1853 0.1775 1.044 0.337
##
## Residual standard error: 0.9733 on 6 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.1537, Adjusted R-squared: 0.01267
## F-statistic: 1.09 on 1 and 6 DF, p-value: 0.3367ggplot(Jiao_2023_pancreas, aes(x = delta_15N_mean, y = ln_PFUnDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFUnDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_pancreas_PFUnDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# PFTrDA
Jiao_2023_pancreas_lm_model_PFTrDA <- lm(ln_PFTrDA_mean ~ delta_15N_mean,
data = Jiao_2023_pancreas)
Jiao_2023_pancreas_PFTrDA_slope <- coef(Jiao_2023_pancreas_lm_model_PFTrDA)["delta_15N_mean"]
summary(Jiao_2023_pancreas_lm_model_PFTrDA)##
## Call:
## lm(formula = ln_PFTrDA_mean ~ delta_15N_mean, data = Jiao_2023_pancreas)
##
## Residuals:
## 9 10 11 12 13 14 15
## -0.41280 1.18172 0.51347 0.46982 0.17417 0.01981 -1.94619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7993 1.5771 0.507 0.634
## delta_15N_mean 0.2606 0.1341 1.944 0.110
##
## Residual standard error: 1.083 on 5 degrees of freedom
## (17 observations deleted due to missingness)
## Multiple R-squared: 0.4304, Adjusted R-squared: 0.3165
## F-statistic: 3.778 on 1 and 5 DF, p-value: 0.1095ggplot(Jiao_2023_pancreas, aes(x = delta_15N_mean, y = ln_PFTrDA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [PFTrDA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_pancreas_PFTrDA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
# 62ClPFESA
Jiao_2023_pancreas_lm_model_62ClPFESA <- lm(ln_62ClPFESA_mean ~ delta_15N_mean,
data = Jiao_2023_pancreas)
Jiao_2023_pancreas_62ClPFESA_slope <- coef(Jiao_2023_pancreas_lm_model_62ClPFESA)["delta_15N_mean"]
summary(Jiao_2023_pancreas_lm_model_62ClPFESA)##
## Call:
## lm(formula = ln_62ClPFESA_mean ~ delta_15N_mean, data = Jiao_2023_pancreas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3829 -0.4758 0.1030 0.7134 0.8250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.59187 1.17773 -0.503 0.6307
## delta_15N_mean 0.29634 0.09858 3.006 0.0198 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8242 on 7 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.5635, Adjusted R-squared: 0.5011
## F-statistic: 9.035 on 1 and 7 DF, p-value: 0.01978ggplot(Jiao_2023_pancreas, aes(x = delta_15N_mean, y = ln_62ClPFESA_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red", linewidth = 0.5) +
labs(x = "delta 15N",
y = "ln [6:2 ClPFESA]") +
theme_minimal() +
annotate("text", x = Inf, y = Inf, label = paste("slope =", round(Jiao_2023_pancreas_62ClPFESA_slope, 3)),
hjust = 3, vjust = 5.5, size = 4, color = "red")
References
- Barghi_2018: Barghi, M., Jin, X., Lee, S., Jeong, Y., Yu, J. P., Paek, W. K., & Moon, H. B. (2018). Accumulation and exposure assessment of persistent chlorinated and fluorinated contaminants in Korean birds. Science of The Total Environment, 645, 220-228.
- Cara_2022: Cara, B., Lies, T., Thimo, G., Robin, L., & Lieven, B. (2022). Bioaccumulation and trophic transfer of perfluorinated alkyl substances (PFAS) in marine biota from the Belgian North Sea: distribution and human health risk implications. Environmental Pollution, 311, 119907.